SRM University. Faculty of Engineering and Technology. Department of Electronics and Communication Engineering
|
|
- Rosamund McLaughlin
- 6 years ago
- Views:
Transcription
1 SRM University Faculty of Engineering and Technology Department of Electronics and Communication Engineering Course code: EC0054 Course title: Neural network and fuzzy Semester: VII Course time: Jan 2011-March 2011 Staff Time table: Day Hour Timing Mon 5 th th th Fri 6 th Location: SRM Tech Park Faculty Details: Name of the Faculty Office Visiting Hours Mail ID Mr. B Srinath Tech Park : am to 4.00 pm Bsrinath86@gmail.com Reference books: 1. Freeman J.A. and Skapura B.M., "Neural Networks, Algorithms Applications and Programming Techniques", Addison-Wesely, George J Klir and Tina A Folger, "Fuzzy sets, uncertainty and information", Prentice Hall of India 3. Laurene Fausett, "Fundamentals of Neural Networks: Architecture, Algorithms and Applications", Pearson Education, H.J. Zimmerman, "Fuzzy set theory and its Applications", Allied Publishers Ltd. Web resources Prerequisite: NIL
2 Objectives 1. To learn the various architectures of building an ANN and its applications 2. Advanced methods of representing information in ANN like self organizing, associative and competitive learning 3. Fundamentals of Crisp sets, Fuzzy sets and Fuzzy Relations Assessment Details Attendance: 5 marks Cycle test I: 10 marks Cycle test II: 10 marks Surprise test I & II: 5 marks Model Exam: 20marks Test schedule Outcomes S.NO Test Portions Duration 1 Cycle test I Unit I and II 2 periods 2 Cycle test II Unit III and IV 2 periods 3 Model exam Full portions 3 hours Students who have successfully completed this course will have full understanding of the following concepts Course Outcome To learn 1. Fundamentals of Artificial Neural Network 2. Processing elements 3. BPN & CPN architecture 4. Learning algorithms for BPN & CPN 5. Basics of fuzzy set Program outcome 1. An ability to understand Artificial Neural Network concept with the help of Biological Neural network 2. Implement algorithms to train ANN by using learning algorithms 3. To test fuzzy set operations and binary relations
3 INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS : Neuro-physiology - General Processing Element - ADALINE - LMS learning rule - MADALINE - XOR Problem - MLP - Back Propagation Network - updation of output and hidden layer weights - application of BPN. Session Name of the Topic Min Ref Teaching Testing No. 1 Neuro-physiology: Biological neural Introduction : Artificial Neural Networks General Processing Element a) Basic building blocks of ANNs b) Terminologies hours method method 1 1 BB Quiz 2 McCulloch-Pitts Neuron Model. 1 1 BB Quiz 3 Learning rules: a) Perceptron b) Delta (LMS) c) Hebb net 4 Adaline and Madaline and its problems 5 Back Propagation : a) Architecture b) Training algorithm c) Selection of parameters d) Learning e) Applications, merits and demerits ASSOCIATIVE MEMORY & CPN: Associative memory - Bi-directional Associative Memory - Hopfield memory - traveling sales man problem Annealing, Boltzmann machine - learning - application - Counter Propagation network -architecture - training - Applications. 6 Associative memory : a) Algorithms for pattern association b) Hetro Associative memory c) Auto Associative memory 7 Bi-directional Associative memory : Architecture, types of nets, application algorithm. 8 Introduction to Feedback Networks: Hopfield nets a) Discrete Hopfield nets b) Continuous Hopfield nets 1 1 BB Test
4 9 Traveling sales man problem and 1 1 BB Test simulated annealing 10 Special Networks: Boltzmann 1 BB Assignment Machine and its learning 11 Counter Propagation Network: a) Fully CPN b) Forward only CPN 1 1 BB Test SELF ORGANIZING MAP & ART: Self-organizing map - learning algorithm - feature map classifier - applications - architecture of Adaptive Resonance Theory - pattern matching in ART network. 12 Self-organizing map : a) Kohonen Self-organizing map b) Learning vector Quantization c) Max and Mexican net d) Hamming net 13 Adaptive Resonance theory: a) Fundamentals b) ART 1 andart2 CRISP SETS AND FUZZY SETS: Introduction - crisp sets an overview - the notion of fuzzy sets -Basic concepts of fuzzy sets - classical logic an overview - Fuzzy logic- Operations on fuzzy sets - fuzzy complement fuzzy union fuzzy intersection combination of operations general aggregation operation. 14 Introduction : Fuzzy logic 2 2 BB Test a) Crisp sets b) Notions of fuzzy sets c) Basic concepts of fuzzy sets d) Classical logic an overview 15 Operations on Fuzzy sets: a) fuzzy complement b) fuzzy union c) fuzzy intersection d) combination of operations e) General aggregation operation. 3 2 BB Test FUZZY RELATIONS Crisp and fuzzy relations - binary relations - binary relations on a single set- equivalence and similarity relations - Compatibility or tolerance relations- orderings - morphisms-fuzzy relation equations 14 Crisp and fuzzy relations 1 2 BB Test 15 Binary relations and binary relations 1 2 BB Test on a single set 16 Equivalence and similarity relations 1 2 BB Test 17 Compatibility or tolerance relations 1 2 BB Test 18 Orderings 1 2 BB Test 19 Morphisms and fuzzy relation equations 2 2 BB Test
5
Evolutive 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 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 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 informationLearning 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 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 informationArtificial Neural Networks
Artificial Neural Networks Andres Chavez Math 382/L T/Th 2:00-3:40 April 13, 2010 Chavez2 Abstract The main interest of this paper is Artificial Neural Networks (ANNs). A brief history of the development
More informationA Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
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 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 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 information*** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE. Proceedings of the 9th Symposium on Legal Data Processing in Europe
*** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE Proceedings of the 9th Symposium on Legal Data Processing in Europe Bonn, 10-12 October 1989 Systems based on artificial intelligence in the legal
More informationSpeaker Identification by Comparison of Smart Methods. Abstract
Journal of mathematics and computer science 10 (2014), 61-71 Speaker Identification by Comparison of Smart Methods Ali Mahdavi Meimand Amin Asadi Majid Mohamadi Department of Electrical Department of Computer
More informationINPE São José dos Campos
INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA
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 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 informationTest Effort Estimation Using Neural Network
J. Software Engineering & Applications, 2010, 3: 331-340 doi:10.4236/jsea.2010.34038 Published Online April 2010 (http://www.scirp.org/journal/jsea) 331 Chintala Abhishek*, Veginati Pavan Kumar, Harish
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 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 informationNeuro-Symbolic Approaches for Knowledge Representation in Expert Systems
Published in the International Journal of Hybrid Intelligent Systems 1(3-4) (2004) 111-126 Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems Ioannis Hatzilygeroudis and Jim Prentzas
More informationThe Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence Algorithms
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS The Method of Immersion the Problem of Comparing Technical Objects in an Expert Shell in the Class of Artificial Intelligence
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 informationMASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE
Master of Science (M.S.) Major in Computer Science 1 MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Major Program The programs in computer science are designed to prepare students for doctoral research,
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 informationSTA 225: Introductory Statistics (CT)
Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic
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 informationSystem Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering
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 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 informationI-COMPETERE: Using Applied Intelligence in search of competency gaps in software project managers.
Information Systems Frontiers manuscript No. (will be inserted by the editor) I-COMPETERE: Using Applied Intelligence in search of competency gaps in software project managers. Ricardo Colomo-Palacios
More informationProposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science
Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science Gilberto de Paiva Sao Paulo Brazil (May 2011) gilbertodpaiva@gmail.com Abstract. Despite the prevalence of the
More informationEvolution of Symbolisation in Chimpanzees and Neural Nets
Evolution of Symbolisation in Chimpanzees and Neural Nets Angelo Cangelosi Centre for Neural and Adaptive Systems University of Plymouth (UK) a.cangelosi@plymouth.ac.uk Introduction Animal communication
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 informationCourse Description. Student Learning Outcomes
Instructor Nancy Lay, Office #2796 Instructor s Campus Phone (760) 355-5707; email = nancy.lay@imperial.edu Office Hours = Mondays and Wednesdays = 10:00-11:00 Tuesdays and Thursdays = 9:45-10:45 N. Lay
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 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 informationSeminar - Organic Computing
Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts
More informationAUTOMATED FABRIC DEFECT INSPECTION: A SURVEY OF CLASSIFIERS
AUTOMATED FABRIC DEFECT INSPECTION: A SURVEY OF CLASSIFIERS Md. Tarek Habib 1, Rahat Hossain Faisal 2, M. Rokonuzzaman 3, Farruk Ahmed 4 1 Department of Computer Science and Engineering, Prime University,
More informationA New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
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 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 informationCOMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS
COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS L. Descalço 1, Paula Carvalho 1, J.P. Cruz 1, Paula Oliveira 1, Dina Seabra 2 1 Departamento de Matemática, Universidade de Aveiro (PORTUGAL)
More informationPredicting Early Students with High Risk to Drop Out of University using a Neural Network-Based Approach
Predicting Early Students with High Risk to Drop Out of University using a Neural Network-Based Approach Miguel Gil, Norma Reyes, María Juárez, Emmanuel Espitia, Julio Mosqueda and Myriam Soria Information
More informationIntroduction to Simulation
Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /
More informationSecond Exam: Natural Language Parsing with Neural Networks
Second Exam: Natural Language Parsing with Neural Networks James Cross May 21, 2015 Abstract With the advent of deep learning, there has been a recent resurgence of interest in the use of artificial neural
More informationUsing focal point learning to improve human machine tacit coordination
DOI 10.1007/s10458-010-9126-5 Using focal point learning to improve human machine tacit coordination InonZuckerman SaritKraus Jeffrey S. Rosenschein The Author(s) 2010 Abstract We consider an automated
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 informationFramewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures
Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures Alex Graves and Jürgen Schmidhuber IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland TU Munich, Boltzmannstr.
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 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 informationA Comparison of Annealing Techniques for Academic Course Scheduling
A Comparison of Annealing Techniques for Academic Course Scheduling M. A. Saleh Elmohamed 1, Paul Coddington 2, and Geoffrey Fox 1 1 Northeast Parallel Architectures Center Syracuse University, Syracuse,
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 informationLearning to Schedule Straight-Line Code
Learning to Schedule Straight-Line Code Eliot Moss, Paul Utgoff, John Cavazos Doina Precup, Darko Stefanović Dept. of Comp. Sci., Univ. of Mass. Amherst, MA 01003 Carla Brodley, David Scheeff Sch. of Elec.
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 informationSpeech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
More informationControl Tutorials for MATLAB and Simulink
Control Tutorials for MATLAB and Simulink Last updated: 07/24/2014 Author Information Prof. Bill Messner Carnegie Mellon University Prof. Dawn Tilbury University of Michigan Asst. Prof. Rick Hill, PhD
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 informationTesting A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA
Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationInternational Journal of Advanced Networking Applications (IJANA) ISSN No. :
International Journal of Advanced Networking Applications (IJANA) ISSN No. : 0975-0290 34 A Review on Dysarthric Speech Recognition Megha Rughani Department of Electronics and Communication, Marwadi Educational
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 informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
More informationPurdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study
Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information
More information1 NETWORKS VERSUS SYMBOL SYSTEMS: TWO APPROACHES TO MODELING COGNITION
NETWORKS VERSUS SYMBOL SYSTEMS 1 1 NETWORKS VERSUS SYMBOL SYSTEMS: TWO APPROACHES TO MODELING COGNITION 1.1 A Revolution in the Making? The rise of cognitivism in psychology, which, by the 1970s, had successfully
More informationPrinciples Of Macroeconomics Case Fair Oster 10e
Case Fair 10e Free PDF ebook Download: Case Fair 10e Download or Read Online ebook principles of macroeconomics case fair oster 10e in PDF Format From The Best User Guide Database is the study of the general
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 informationEGRHS Course Fair. Science & Math AP & IB Courses
EGRHS Course Fair Science & Math AP & IB Courses Science Courses: AP Physics IB Physics SL IB Physics HL AP Biology IB Biology HL AP Physics Course Description Course Description AP Physics C (Mechanics)
More informationSAM - Sensors, Actuators and Microcontrollers in Mobile Robots
Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2017 230 - ETSETB - Barcelona School of Telecommunications Engineering 710 - EEL - Department of Electronic Engineering BACHELOR'S
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 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 informationHIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION
HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION Atul Laxman Katole 1, Krishna Prasad Yellapragada 1, Amish Kumar Bedi 1, Sehaj Singh Kalra 1 and Mynepalli Siva Chaitanya 1 1 Samsung
More informationAutoregressive product of multi-frame predictions can improve the accuracy of hybrid models
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationInternational Journal of Computational Intelligence and Informatics, Vol. 1 : No. 4, January - March 2012
Text-independent Mono and Cross-lingual Speaker Identification with the Constraint of Limited Data Nagaraja B G and H S Jayanna Department of Information Science and Engineering Siddaganga Institute of
More informationAccelerated Learning Course Outline
Accelerated Learning Course Outline Course Description The purpose of this course is to make the advances in the field of brain research more accessible to educators. The techniques and strategies of Accelerated
More informationTime series prediction
Chapter 13 Time series prediction Amaury Lendasse, Timo Honkela, Federico Pouzols, Antti Sorjamaa, Yoan Miche, Qi Yu, Eric Severin, Mark van Heeswijk, Erkki Oja, Francesco Corona, Elia Liitiäinen, Zhanxing
More informationDeep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach
#BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying
More informationPhonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project
Phonetic- and Speaker-Discriminant Features for Speaker Recognition by Lara Stoll Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, University of California
More informationEarly Model of Student's Graduation Prediction Based on Neural Network
TELKOMNIKA, Vol.12, No.2, June 2014, pp. 465~474 ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013 DOI: 10.12928/TELKOMNIKA.v12i2.1603 465 Early Model of Student's Graduation Prediction
More informationTHE UNIVERSITY OF SYDNEY Semester 2, Information Sheet for MATH2068/2988 Number Theory and Cryptography
THE UNIVERSITY OF SYDNEY Semester 2, 2017 Information Sheet for MATH2068/2988 Number Theory and Cryptography Websites: It is important that you check the following webpages regularly. Intermediate Mathematics
More informationAccelerated Learning Online. Course Outline
Accelerated Learning Online Course Outline Course Description The purpose of this course is to make the advances in the field of brain research more accessible to educators. The techniques and strategies
More informationIAT 888: Metacreation Machines endowed with creative behavior. Philippe Pasquier Office 565 (floor 14)
IAT 888: Metacreation Machines endowed with creative behavior Philippe Pasquier Office 565 (floor 14) pasquier@sfu.ca Outline of today's lecture A little bit about me A little bit about you What will that
More informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationCourse outline. Code: HLT100 Title: Anatomy and Physiology
Course outline Code: HLT100 Title: Anatomy and Physiology Faculty of: Science, Health, Education and Engineering Teaching Session: Semester 2 Year: 2017 Course Coordinator: Ann Framp Email: aframp@usc.edu.au
More informationIssues in the Mining of Heart Failure Datasets
International Journal of Automation and Computing 11(2), April 2014, 162-179 DOI: 10.1007/s11633-014-0778-5 Issues in the Mining of Heart Failure Datasets Nongnuch Poolsawad 1 Lisa Moore 1 Chandrasekhar
More informationAnswer Key Applied Calculus 4
Answer Key Applied Calculus 4 Free PDF ebook Download: Answer Key 4 Download or Read Online ebook answer key applied calculus 4 in PDF Format From The Best User Guide Database CALCULUS. FOR THE for the
More informationPH.D. IN COMPUTER SCIENCE PROGRAM (POST M.S.)
PH.D. IN COMPUTER SCIENCE PROGRAM (POST M.S.) OVERVIEW ADMISSION REQUIREMENTS PROGRAM REQUIREMENTS OVERVIEW FOR THE PH.D. IN COMPUTER SCIENCE Overview The doctoral program is designed for those students
More informationCollege Pricing. Ben Johnson. April 30, Abstract. Colleges in the United States price discriminate based on student characteristics
College Pricing Ben Johnson April 30, 2012 Abstract Colleges in the United States price discriminate based on student characteristics such as ability and income. This paper develops a model of college
More informationAn empirical study of learning speed in backpropagation
Carnegie Mellon University Research Showcase @ CMU Computer Science Department School of Computer Science 1988 An empirical study of learning speed in backpropagation networks Scott E. Fahlman Carnegie
More informationSyllabus: CS 377 Communication and Ethical Issues in Computing 3 Credit Hours Prerequisite: CS 251, Data Structures Fall 2015
Syllabus: CS 377 Communication and Ethical Issues in Computing 3 Credit Hours Prerequisite: CS 251, Data Structures Fall 2015 Instructor: Robert H. Sloan Website: http://www.cs.uic.edu/sloan Office: 1112
More informationMTH 215: Introduction to Linear Algebra
MTH 215: Introduction to Linear Algebra Fall 2017 University of Rhode Island, Department of Mathematics INSTRUCTOR: Jonathan A. Chávez Casillas E-MAIL: jchavezc@uri.edu LECTURE TIMES: Tuesday and Thursday,
More informationDeep Neural Network Language Models
Deep Neural Network Language Models Ebru Arısoy, Tara N. Sainath, Brian Kingsbury, Bhuvana Ramabhadran IBM T.J. Watson Research Center Yorktown Heights, NY, 10598, USA {earisoy, tsainath, bedk, bhuvana}@us.ibm.com
More informationEcole Polytechnique Fédérale de Lausanne EPFL School of Computer and Communication Sciences IC. School of Computer and Communication Sciences
Ecole Polytechnique Fédérale de Lausanne EPFL School of Computer and Communication Sciences IC 1 WELCOME to the Master programs in Computer Science, Data Science and Communication Systems 2 TODAY S SPEAKERS
More information(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
More informationAnalysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription
Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription Wilny Wilson.P M.Tech Computer Science Student Thejus Engineering College Thrissur, India. Sindhu.S Computer
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 informationUsing the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the SAT
The Journal of Technology, Learning, and Assessment Volume 6, Number 6 February 2008 Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees Cognitive Skills in Algebra on the
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 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 informationA Review: Speech Recognition with Deep Learning Methods
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 5, May 2015, pg.1017
More informationarxiv: v1 [cs.cv] 10 May 2017
Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University
More information