Proceedings of the 8th WSEAS International Conference on Applied Computer and Applied Computational Science. Boolean Conversion
|
|
- Tracy Clarke
- 6 years ago
- Views:
Transcription
1 Boolean Conversion Fengming M. Chang Department of Information Science and Applications Asia University Wufeng, Taichung County, Taiwan Abstract: - The Boolean Conversion (BC) is a novel method that is proposed in this paper solve the problem of large number of attributes in machine learning. Large number of data attributes is easy cause a system freezes or shuts down, especially in the neuro-fuzzy based learning. The purpose of BC is reduce data dimensions by a binary or Boolean conversion process. All the attributes are reserved but combined in few numbers of new attributes instead of that some attributes are removed. Three data sets, nbuses, ACLP, and MONK, are offered in this study test and compare the learning accuracies and learning. The results indicate that the proposed BC can keep about the same level of but increase the learning efficiency. Key-Words: - Boolean conversion, Large attribute, Machine learning, Neuro-fuzzy, fuzzificain Introduction Recent have shown widely applications in the fields of Artificial intelligence (AI) for data classification or prediction [-4]. Many methods were proposed. For most of the examples in previous studies, the number of input attributes is not large. They probably only provide a theoretic model for researchers. However, real data in some theoretic studies and some practical applications have plenty of input attributes. It causes some problems. First, some systems will easily shut down because the calculations of the machine learning are o large. Second, some learning programs have their limits. The learning methods that mostly need reduce input attribute numbers are Artificial Neural Network (ANN), Fuzzy Neural Network (FNN, neuro-fuzzy), and, the later is improved based on FNN. FNN and are more difficult perform than ANN. FNN deals with the network learning using fuzzy membership functions. Because the de calculations in FNN are complex and difficult, most of the fuzzy membership functions are setup as triangular, generalized bell, trapezoidal, and so on so that they are easy calculate. Anomalous shapes fuzzy membership is not recommended because it is almost impossible defuzzify using programs beforehand even though the defuzzy calculation is still not efficient. When the input attribute amounts are larger than 6, the FNN program could not perform normally. Most of the, the computer went on hold without any response, it froze. In this article, nbuses data set has 9 input attributes. These data are used as fail for learning when using FNN or methods because they have o many attributes. On the other hand, some machine learning programs have upper limit of network nodes, and reduction of the input attribute amounts is also necessary. Literatures Review Data attribute reduction is an important way improve the efficiency of AI learning. Early related work was done by Shen and Chouchoulas, who proposed a Rough Set Attribute Reduction (RSAR) method remove redundant input attributes for discrete s from complex systems. However, RSAR still lacks efficiency although it can reduct attributes [5]. The other study is that Beynon introduced an approximate reducts concept and proposed a Variable Precision Rough Sets (VPRS) model find out the smallest set of attributes [6]. Later, Hsu et al. applied VPRS model for mobile phone test procedure [7]. Inbarani et al. also applied VPRS for feature selection of web usage mining [8]. In addition, Ang and Quek did not reduce data attriubute but reduce fuzzy rules by combined rough set and neuro-fuzzy learning [9]. The Proposed Method First, a data set of nbuses [] that consists of 9 input and output attributes is used explain the proposed BC method first. The nbuses can not be performed well in FNN and mega- methods. Values of its attributes are integers. The s of the first attribute are {,,, 4}, s of the second, the 8 th, and output attributes are {,, }, and s of the other attributes are {, }. The process of the BC method is simple. Each ISSN: ISBN:
2 decimal number can be transferred in a Boolean number one on one mapping. For example, in the first instance of our nbuses data, 9 attributes are combined in new attributes. We combine the first the third attributes be the first new attribute. On the left of the Fig., decimal numbers, 4,, and, are transferred in Boolean numbers accordingly. Considering the maximum of each attribute, Boolean number for decimal number should be. So that the number of bit in Boolean number for each attribute is fixed. Next, the Boolean numbers are physically combined be a unique Boolean number as shown in the middle part of Fig.. Each original Boolean number occupies its own digital position in the combined Boolean number format without mixing with other numbers. After that, for the convenience of calculation in the real world, this combined Boolean number is transferred a decimal number. In the above process, the input s are combined in a unique decimal 77 and the input attributes are reduced. The reason for not combining the decimal number directly, such as combine 4,, be 4 is because that 4 is bigger than 77, the result of BC. Smaller number is easier for calculation. decimal numbers 4 Transferred Boolean numbers Combined 6 Boolean numbers one Transferred a decimal numbers 77 Fig.. The process of the Boolean Conversion. With Fig. as an example, there are inputs were converted in a single new input. First, the original inputs {4,, } are converted in a Boolean digit number that is {,, }. Second, these Boolean digit numbers are physically combined in one Boolean digit number:. The corresponding decimal number is 77. It can be expressed by the binary system as: {,, } = * + * + * It could be a Boolean weight expressed by Boolean system as: B = [ ] or expressed by decimal system: 4 { 4,,} 77 = 4 * + * + * and the binary weight vecr is B = [ 4 power of is determined by the data maximum domain. For example, if the range of the second attribute is from 6, the maximum domain is 6, then 6( decimal) = ( binary) = ] + ( decimal) ( decimal) means the number of bits the second attribute needs is, bits ( ) =. Each attribute may not need the same number of bit in other cases. The power of the second attribute is the bit number plus the power of the third attribute or the tal bit number from the third the fifth inputs. It is presented as: power( i) = bits( i + ) + power( i + ) = i ( I ), power ( I) =, where I is the tal number of the input i. I m= i+ bits( m) 4 Results and Comparisons In this study, three data sets are offered check learning accuracies and of the results of both non-applying and applying BC. These data sets are nbuses, ACLP, and MONK. 4. nbuses data The nbuese data that has been mentioned in section is used by the proposed method in this subsection. Table shows one record of the data. There are 9 input and one output attributes in the data. The st the rd attributes are converted in a new input attribute, the 4 th the 6 th attributes are combined in the nd new input by BC, and the 7 th the 9 th are combined in the rd new one. Therefore there are only three new input attributes. As shown in Table, the new input record is {77, 8, 9}. After all attributes are converted using BC, the data are tested and compared using BN, C4.5, SVM, ANN, FNN, and methods with -folds cross-validation testing. Each fold are used as testing data in turn and the remaining tal of 9 folds data are used as training data. The results are presented in ISSN: ISBN:
3 Table. Without using BC, FNN and fail perform. After applying BC, it can easily perform machine learning using FNN and methods. Most of the prediction accuracies after using BC are even a little higher than without using it in this case, and learning decreases. Fig. compares the prediction accuracies under different learning methods. Fig.. illustrates the learning. For nbuses data, even after using BC, for FNN and mega- is still large. For other learning methods, learning reduced. Table. An explanation of converting 9 attributes new decimal attributes. # # # #4 #5 #6 #7 #8 #9 Original decimal Converse Boolean Combine three Boolean Converse three decimal Table. The comparison of nbuses data. Non Accuracy 9.4 % 9.4% 86.84% 85.5% Fail Fail -BC Time(sec) perform perform Accuracy 94.74% 9.4% 84.% 9.% 95% 95% BC Time(sec) learning without using BC learning after using BC Fig.. The learning comparison before and after using BC method by six methods for nbuses data. 4. ACLP data There are in tal 4 instance in ACLP data with 6 input and output attributes. The st, the 5 th, and the 6 th attributes have s of {,, }, and the other attributes have s of {, }. For neuro-fuzzy learning, 6 input attributes cause learning process very slowly. Fortunately, the s of each attribute are not large. We can compare the results of FNN and mega- methods. The results are presented in Table, Fig. 4, and Fig. 5. Accuracies after using BC are a little lower than before, but is saving. Before applying BC in FNN and mega-, for learning is very large. After using BC, is saved largely. Table. The comparison of ACLP data. Non Accuracy 86.4 % 87.86% 9.7% 84.9% 84.7% 84.7% -BC Time(sec) Accuracy 8.7% 89.9% 8.4% 8.4% 8.8% 8.% BC Time(sec) %.% 8.% 6.% 4.%.%.% without using BC after using BC Fig.. The comparison before and after using BC method by six methods for nbuses data. 8.% 6.% 4.%.%.% without using BC after using BC Fig. 4. The comparison before and after using BC method by six methods for ACLP data. ISSN: ISBN:
4 learning without using BC learning after using BC Fig. 5. The learning comparison before and after using BC method by six methods for ACLP data learning without using BC learning after using BC Fig. 7. The learning comparison before and after using BC method by six methods for Monk data. 4. Monk data Monk data were created by Sebastian Thrun (see UCI Machine Learning Reposiry []) which has 4 instances, 6 inputs and output attributes. Because the number of attributes is not large in this case, we can compare the learning accuracies of FNN and mega-fuzzificatioin with and without using BC again. Table 4 shows the results. In this case, FNN and mega- can be performed but waste large before using BC. All the accuracies after using BC are a little lower than before. The learning accuracies are also compared in Fig. 6 and learning is compared in Fig. 7. Still, leaning before using BC for FNN and mega- is very large, but becomes very small after applying BC. Table 4. The comparison of Monk data. Accuracy 9.6 % % 8.56% % % % Non-BC Time(sec) Accuracy 89.% 96.6% 76.9% 98.87% 97% 98% BC Time(sec)..6.% 8.% 6.% 4.%.%.% without using BC after using BC Fig. 6. The comparison before and after using BC method by six methods for Monk data. 5 Conclusions In this study, a novel BC method is proposed deal with the problem of that data with a large number of attributes may cause a system freezes or shuts down. BC reduce attribute number by combining some of the attributes in smaller number of new attributes instead of that removing some attributes from data. After attributes are combined and reduced, learning accuracies and learning are compared by BN, C4.5, SVM, ANN, neuro-fuzzy, and learning methods. In this study, three data sets, nbuses, ACLP, and MONK, are offered test and compare the learning results. Some of their learning accuracies after using BC are a little lower than before, some have a little higher accuracies. In general, the learning after applying BC is not worse. In addition, leaning is shortened after BC is used. Facing the problem of fail perform in neuro-fuzzy, the proposed BC method indeed solves the problem of data have large attributes in learning in brief. Acknowledgement Thanks are due the support in part by the National Science Council of Taiwan under Grant No. NSC H MY. References: [] Y.Y. Yao, Granular computing: basic issues and possible solutions, Proceedings of the 5 th Joint Conference on Information Sciences, 999, pp [] L. Polkowski and A. Skowron, Towards adaptive calculus of granules, Proceedings of 998 IEEE International Conference on Fuzzy Systems, pp. 6. [] T.Y. Lin, Granular computing on binary relations I: data mining and neighborhood systems, II: Rough set representations and belief functions, in ISSN: ISBN:
5 L. Polkowski and A. Skowron eds., Rough sets in knowledge discovery. Heidelberg, Physica-Verlag, 998, pp [4] Y.Y. Yao, Granular computing using neighborhood systems, in R. Roy, T. Furuhashi, and P.K. Chawdhry (eds.) Advances in Soft Computing: Engineering Design and Manufacturing, Springer-Verlag, London, 999, pp [5] T.Y. Lin, Data mining: granular computing approach, Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, 999, pp. 4. [6] A. Skowron and J. Stepaniuk, Information granules: wards foundations of granular computing, International Journal of Intelligent Systems, Vol. 6, 57 85,. [7] Y.Y. Yao, Information granulation and rough set approximation, International Journal of Intelligent Systems, Vol. 6, 87 4,. [8] J.-S. R. Jang, ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on System, Man, and Cybernetics, vol., no., pp , 99. [9] D. C. Li, C. Wu, and F. M. Chang, Using data-fuzzifying technology in small data set learning improve FMS scheduling, International Journal of Advanced Manufacturing Technology, vol. 7, no. -4, pp. -8, 5. []F. M. Chang, and C. C. Chan, Improve Neuro-Fuzzy Learning by Attribute Reduction, The 7 th Annual Meeting of the North American Fuzzy Information Processing Society, The Rockefeller University, NY, USA, May 8-, 8. []B. Predki, R. Slowinski, J. Stefanowski, R. Susmaga, and Sz. Wilk, ROSE - Software Implementation of the Rough Set Theory, In: L. Polkowski, A. Skowron, eds, Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, vol. 44, pp , []B. Predki and Sz.Wilk, Rough Set Based Data Exploration Using ROSE System, In: Z. W. Ras, A. Skowron, eds, Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence, vol. 69, pp.7-8, 999. []A. Øhrn and J. Komorowski, ROSETTA: a rough set olkit for analysis of data, Proc. Third International Joint Conference on Information Sciences, Vol., pp. 4-47, Durham, NC, March 997. [4]Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer, 99. [5]S. Qiang, and C. Alexios, A modular approach generating fuzzy rules with reduced attributes for the moniring of complex systems, Engineering Applications of Artificial Intelligence, vol., No., pp.6-78,. [6]M. Beynon, Reducts within the variable precision rough set model: A further investigation, European Journal of Operational Research, vol. 4, pp.59-65,. [7]J. H. Hsu, T. L. Chiang, and H. C. Wang, VPRS model for mobile phone test procedure, Journal of the Chinese Institute of Industrial Engineers, vol., no. 4, pp.45-55, 6. [8]H. H. Inbarani, K. Thangavel, and A. Pethalakshmi, Rough set based Feature Selection for Web Usage Mining, International Conference on Computational Intelligence and Muldia Applications, pp.-8, 7. [9]K. K. Ang, and C. Quek, Sck Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach, IEEE Transactions on Neural Network, vol. 7, no. 5, pp.-5, 6. []Laborary of Intelligent Decision Support Systems, Poznan University of Technology, []UCI Machine Learning Reposiry, ISSN: ISBN:
Learning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationPh.D in Advance Machine Learning (computer science) PhD submitted, degree to be awarded on convocation, sept B.Tech in Computer science and
Name Qualification Sonia Thomas Ph.D in Advance Machine Learning (computer science) PhD submitted, degree to be awarded on convocation, sept. 2016. M.Tech in Computer science and Engineering. B.Tech in
More informationEvolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationAnalysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems
Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Ajith Abraham School of Business Systems, Monash University, Clayton, Victoria 3800, Australia. Email: ajith.abraham@ieee.org
More informationRule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
More informationWhat s in a Step? Toward General, Abstract Representations of Tutoring System Log Data
What s in a Step? Toward General, Abstract Representations of Tutoring System Log Data Kurt VanLehn 1, Kenneth R. Koedinger 2, Alida Skogsholm 2, Adaeze Nwaigwe 2, Robert G.M. Hausmann 1, Anders Weinstein
More informationCurriculum Vitae of Chiang-Ju Chien
Contact Information Curriculum Vitae of Chiang-Ju Chien Affiliation : Department of Electronic Engineering, Huafan University, Taiwan Address : Department of Electronic Engineering, Huafan University,
More informationKnowledge-Based - Systems
Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University
More informationApplying Fuzzy Rule-Based System on FMEA to Assess the Risks on Project-Based Software Engineering Education
Journal of Software Engineering and Applications, 2017, 10, 591-604 http://www.scirp.org/journal/jsea ISSN Online: 1945-3124 ISSN Print: 1945-3116 Applying Fuzzy Rule-Based System on FMEA to Assess the
More informationLearning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationThe 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X
The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,
More informationAustralian Journal of Basic and Applied Sciences
AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean
More informationData Fusion Models in WSNs: Comparison and Analysis
Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,
More informationSoftprop: Softmax Neural Network Backpropagation Learning
Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science
More informationReducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
More informationSpeech Emotion Recognition Using Support Vector Machine
Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,
More informationKamaldeep Kaur University School of Information Technology GGS Indraprastha University Delhi
Soft Computing Approaches for Prediction of Software Maintenance Effort Dr. Arvinder Kaur University School of Information Technology GGS Indraprastha University Delhi Kamaldeep Kaur University School
More informationLongest Common Subsequence: A Method for Automatic Evaluation of Handwritten Essays
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. IV (Nov Dec. 2015), PP 01-07 www.iosrjournals.org Longest Common Subsequence: A Method for
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationFSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification arxiv:1709.09268v2 [cs.lg] 15 Nov 2017 Kamran Kowsari, Nima Bari, Roman Vichr and Farhad A. Goodarzi Department of Computer
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationAUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS
AUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS R.Barco 1, R.Guerrero 2, G.Hylander 2, L.Nielsen 3, M.Partanen 2, S.Patel 4 1 Dpt. Ingeniería de Comunicaciones. Universidad de Málaga.
More informationSARDNET: A Self-Organizing Feature Map for Sequences
SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu
More informationLearning Methods in Multilingual Speech Recognition
Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex
More informationClass-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification
Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Tomi Kinnunen and Ismo Kärkkäinen University of Joensuu, Department of Computer Science, P.O. Box 111, 80101 JOENSUU,
More informationMining Association Rules in Student s Assessment Data
www.ijcsi.org 211 Mining Association Rules in Student s Assessment Data Dr. Varun Kumar 1, Anupama Chadha 2 1 Department of Computer Science and Engineering, MVN University Palwal, Haryana, India 2 Anupama
More informationVisual CP Representation of Knowledge
Visual CP Representation of Knowledge Heather D. Pfeiffer and Roger T. Hartley Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001, USA email: hdp@cs.nmsu.edu and rth@cs.nmsu.edu
More informationImpact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees
Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Mariusz Łapczy ski 1 and Bartłomiej Jefma ski 2 1 The Chair of Market Analysis and Marketing Research,
More informationPredicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks
Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim Samsung Electronics Co., Ltd. Seoul, South Korea {dev.chaplot,eunhee.rhim,jihie.kim}@samsung.com
More informationAn OO Framework for building Intelligence and Learning properties in Software Agents
An OO Framework for building Intelligence and Learning properties in Software Agents José A. R. P. Sardinha, Ruy L. Milidiú, Carlos J. P. Lucena, Patrick Paranhos Abstract Software agents are defined as
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationData Integration through Clustering and Finding Statistical Relations - Validation of Approach
Data Integration through Clustering and Finding Statistical Relations - Validation of Approach Marek Jaszuk, Teresa Mroczek, and Barbara Fryc University of Information Technology and Management, ul. Sucharskiego
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationA student diagnosing and evaluation system for laboratory-based academic exercises
A student diagnosing and evaluation system for laboratory-based academic exercises Maria Samarakou, Emmanouil Fylladitakis and Pantelis Prentakis Technological Educational Institute (T.E.I.) of Athens
More informationPractical Integrated Learning for Machine Element Design
Practical Integrated Learning for Machine Element Design Manop Tantrabandit * Abstract----There are many possible methods to implement the practical-approach-based integrated learning, in which all participants,
More informationWord Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationThe Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma
International Journal of Computer Applications (975 8887) The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma Gilbert M.
More informationClassification Using ANN: A Review
International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 7 (2017), pp. 1811-1820 Research India Publications http://www.ripublication.com Classification Using ANN:
More informationA Reinforcement Learning Variant for Control Scheduling
A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement
More informationInternational Series in Operations Research & Management Science
International Series in Operations Research & Management Science Volume 240 Series Editor Camille C. Price Stephen F. Austin State University, TX, USA Associate Series Editor Joe Zhu Worcester Polytechnic
More informationKnowledge Transfer in Deep Convolutional Neural Nets
Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract
More informationAUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION
JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationA SURVEY OF FUZZY COGNITIVE MAP LEARNING METHODS
A SURVEY OF FUZZY COGNITIVE MAP LEARNING METHODS Wociech Stach, Lukasz Kurgan, and Witold Pedrycz Department of Electrical and Computer Engineering University of Alberta Edmonton, Alberta T6G 2V4, Canada
More informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationCLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH
ISSN: 0976-3104 Danti and Bhushan. ARTICLE OPEN ACCESS CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH Ajit Danti 1 and SN Bharath Bhushan 2* 1 Department
More informationUsing Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models
Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models Dimitris Kalles and Christos Pierrakeas Hellenic Open University,
More informationLecture Notes on Mathematical Olympiad Courses
Lecture Notes on Mathematical Olympiad Courses For Junior Section Vol. 2 Mathematical Olympiad Series ISSN: 1793-8570 Series Editors: Lee Peng Yee (Nanyang Technological University, Singapore) Xiong Bin
More informationDeveloping True/False Test Sheet Generating System with Diagnosing Basic Cognitive Ability
Developing True/False Test Sheet Generating System with Diagnosing Basic Cognitive Ability Shih-Bin Chen Dept. of Information and Computer Engineering, Chung-Yuan Christian University Chung-Li, Taiwan
More informationConversational Framework for Web Search and Recommendations
Conversational Framework for Web Search and Recommendations Saurav Sahay and Ashwin Ram ssahay@cc.gatech.edu, ashwin@cc.gatech.edu College of Computing Georgia Institute of Technology Atlanta, GA Abstract.
More informationA Case-Based Approach To Imitation Learning in Robotic Agents
A Case-Based Approach To Imitation Learning in Robotic Agents Tesca Fitzgerald, Ashok Goel School of Interactive Computing Georgia Institute of Technology, Atlanta, GA 30332, USA {tesca.fitzgerald,goel}@cc.gatech.edu
More informationRule discovery in Web-based educational systems using Grammar-Based Genetic Programming
Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de
More informationHandling Concept Drifts Using Dynamic Selection of Classifiers
Handling Concept Drifts Using Dynamic Selection of Classifiers Paulo R. Lisboa de Almeida, Luiz S. Oliveira, Alceu de Souza Britto Jr. and and Robert Sabourin Universidade Federal do Paraná, DInf, Curitiba,
More informationA Study of Metacognitive Awareness of Non-English Majors in L2 Listening
ISSN 1798-4769 Journal of Language Teaching and Research, Vol. 4, No. 3, pp. 504-510, May 2013 Manufactured in Finland. doi:10.4304/jltr.4.3.504-510 A Study of Metacognitive Awareness of Non-English Majors
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
More informationA study of speaker adaptation for DNN-based speech synthesis
A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,
More informationTransfer Learning Action Models by Measuring the Similarity of Different Domains
Transfer Learning Action Models by Measuring the Similarity of Different Domains Hankui Zhuo 1, Qiang Yang 2, and Lei Li 1 1 Software Research Institute, Sun Yat-sen University, Guangzhou, China. zhuohank@gmail.com,lnslilei@mail.sysu.edu.cn
More informationProduct Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments
Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments Vijayshri Ramkrishna Ingale PG Student, Department of Computer Engineering JSPM s Imperial College of Engineering &
More informationCommunity-oriented Course Authoring to Support Topic-based Student Modeling
Community-oriented Course Authoring to Support Topic-based Student Modeling Sergey Sosnovsky, Michael Yudelson, Peter Brusilovsky School of Information Sciences, University of Pittsburgh, USA {sas15, mvy3,
More informationQuantitative Evaluation of an Intuitive Teaching Method for Industrial Robot Using a Force / Moment Direction Sensor
International Journal of Control, Automation, and Systems Vol. 1, No. 3, September 2003 395 Quantitative Evaluation of an Intuitive Teaching Method for Industrial Robot Using a Force / Moment Direction
More informationEffectiveness of Electronic Dictionary in College Students English Learning
2016 International Conference on Mechanical, Control, Electric, Mechatronics, Information and Computer (MCEMIC 2016) ISBN: 978-1-60595-352-6 Effectiveness of Electronic Dictionary in College Students English
More informationMachine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler
Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina
More informationCourse Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE
EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers
More informationMulti-Lingual Text Leveling
Multi-Lingual Text Leveling Salim Roukos, Jerome Quin, and Todd Ward IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 {roukos,jlquinn,tward}@us.ibm.com Abstract. Determining the language proficiency
More informationDepartment of Computer Science GCU Prospectus
Department of Computer Science GCU Prospectus 2015 59 Introduction In recent years, the immense growth of numerous industries resulted in the instant need for young and vigorous IT professionals, who could
More informationAxiom 2013 Team Description Paper
Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association
More informationMath Placement at Paci c Lutheran University
Math Placement at Paci c Lutheran University The Art of Matching Students to Math Courses Professor Je Stuart Math Placement Director Paci c Lutheran University Tacoma, WA 98447 USA je rey.stuart@plu.edu
More informationAutomating the E-learning Personalization
Automating the E-learning Personalization Fathi Essalmi 1, Leila Jemni Ben Ayed 1, Mohamed Jemni 1, Kinshuk 2, and Sabine Graf 2 1 The Research Laboratory of Technologies of Information and Communication
More informationChapter 4 - Fractions
. Fractions Chapter - Fractions 0 Michelle Manes, University of Hawaii Department of Mathematics These materials are intended for use with the University of Hawaii Department of Mathematics Math course
More informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationDinesh K. Sharma, Ph.D. Department of Management School of Business and Economics Fayetteville State University
Department of Management School of Business and Economics Fayetteville State University EDUCATION Doctor of Philosophy, Devi Ahilya University, Indore, India (2013) Area of Specialization: Management:
More informationUse of Online Information Resources for Knowledge Organisation in Library and Information Centres: A Case Study of CUSAT
DESIDOC Journal of Library & Information Technology, Vol. 31, No. 1, January 2011, pp. 19-24 2011, DESIDOC Use of Online Information Resources for Knowledge Organisation in Library and Information Centres:
More informationDigital Fabrication and Aunt Sarah: Enabling Quadratic Explorations via Technology. Michael L. Connell University of Houston - Downtown
Digital Fabrication and Aunt Sarah: Enabling Quadratic Explorations via Technology Michael L. Connell University of Houston - Downtown Sergei Abramovich State University of New York at Potsdam Introduction
More informationAn Introduction to Simio for Beginners
An Introduction to Simio for Beginners C. Dennis Pegden, Ph.D. This white paper is intended to introduce Simio to a user new to simulation. It is intended for the manufacturing engineer, hospital quality
More informationConstructive Induction-based Learning Agents: An Architecture and Preliminary Experiments
Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Ibrahim F. Imam and Janusz Wnek (Eds.), pp. 38-51, Melbourne Beach, Florida, 1995. Constructive Induction-based
More informationCHAPTER 4: REIMBURSEMENT STRATEGIES 24
CHAPTER 4: REIMBURSEMENT STRATEGIES 24 INTRODUCTION Once state level policymakers have decided to implement and pay for CSR, one issue they face is simply how to calculate the reimbursements to districts
More informationIntegrating E-learning Environments with Computational Intelligence Assessment Agents
Integrating E-learning Environments with Computational Intelligence Assessment Agents Christos E. Alexakos, Konstantinos C. Giotopoulos, Eleni J. Thermogianni, Grigorios N. Beligiannis and Spiridon D.
More informationADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF
Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationProcedia - Social and Behavioral Sciences 237 ( 2017 )
Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Sciences 237 ( 2017 ) 613 617 7th International Conference on Intercultural Education Education, Health and ICT
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationAGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS
AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS 1 CALIFORNIA CONTENT STANDARDS: Chapter 1 ALGEBRA AND WHOLE NUMBERS Algebra and Functions 1.4 Students use algebraic
More informationFUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria
FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate
More informationPREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES
PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES Po-Sen Huang, Kshitiz Kumar, Chaojun Liu, Yifan Gong, Li Deng Department of Electrical and Computer Engineering,
More informationIdentification of Opinion Leaders Using Text Mining Technique in Virtual Community
Identification of Opinion Leaders Using Text Mining Technique in Virtual Community Chihli Hung Department of Information Management Chung Yuan Christian University Taiwan 32023, R.O.C. chihli@cycu.edu.tw
More informationDetecting Wikipedia Vandalism using Machine Learning Notebook for PAN at CLEF 2011
Detecting Wikipedia Vandalism using Machine Learning Notebook for PAN at CLEF 2011 Cristian-Alexandru Drăgușanu, Marina Cufliuc, Adrian Iftene UAIC: Faculty of Computer Science, Alexandru Ioan Cuza University,
More informationQuickStroke: An Incremental On-line Chinese Handwriting Recognition System
QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents
More informationIMGD Technical Game Development I: Iterative Development Techniques. by Robert W. Lindeman
IMGD 3000 - Technical Game Development I: Iterative Development Techniques by Robert W. Lindeman gogo@wpi.edu Motivation The last thing you want to do is write critical code near the end of a project Induces
More informationAn Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method
Farhadi F, Sorkhi M, Hashemi S et al. An effective framework for fast expert mining in collaboration networks: A grouporiented and cost-based method. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(3): 577
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationA NEW ALGORITHM FOR GENERATION OF DECISION TREES
TASK QUARTERLY 8 No 2(2004), 1001 1005 A NEW ALGORITHM FOR GENERATION OF DECISION TREES JERZYW.GRZYMAŁA-BUSSE 1,2,ZDZISŁAWS.HIPPE 2, MAKSYMILIANKNAP 2 ANDTERESAMROCZEK 2 1 DepartmentofElectricalEngineeringandComputerScience,
More informationPROCEEDINGS OF SPIE. Double degree master program: Optical Design
PROCEEDINGS OF SPIE SPIEDigitalLibrary.org/conference-proceedings-of-spie Double degree master program: Optical Design Alexey Bakholdin, Malgorzata Kujawinska, Irina Livshits, Adam Styk, Anna Voznesenskaya,
More informationAgent-Based Software Engineering
Agent-Based Software Engineering Learning Guide Information for Students 1. Description Grade Module Máster Universitario en Ingeniería de Software - European Master on Software Engineering Advanced Software
More informationMachine Learning from Garden Path Sentences: The Application of Computational Linguistics
Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,
More informationDIDACTIC MODEL BRIDGING A CONCEPT WITH PHENOMENA
DIDACTIC MODEL BRIDGING A CONCEPT WITH PHENOMENA Beba Shternberg, Center for Educational Technology, Israel Michal Yerushalmy University of Haifa, Israel The article focuses on a specific method of constructing
More informationTeam Formation for Generalized Tasks in Expertise Social Networks
IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust Team Formation for Generalized Tasks in Expertise Social Networks Cheng-Te Li Graduate
More informationCalibration of Confidence Measures in Speech Recognition
Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE
More informationMatching Similarity for Keyword-Based Clustering
Matching Similarity for Keyword-Based Clustering Mohammad Rezaei and Pasi Fränti University of Eastern Finland {rezaei,franti}@cs.uef.fi Abstract. Semantic clustering of objects such as documents, web
More information