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Nanjing University of Science & Technology

Like to Thank

Lu Jian FengYang Jing-yu Wang Han

For inviting me to present this course on

Pattern Recognition: Statistical and Neural

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Nanjing University of Science & Technology

Lecture 1: Introduction

September 14, 2005

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Today’s  Presentation ______________________

Introduce Myself

Proposed Course

Current Research

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Dr. Lonnie C. Ludeman

Professor Emeritus, College Professor

Klipsch School of Electrical and computer EngineeringNew Mexico State UniversityLas Cruces, New Mexico 88003USA

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Textbooks:

Fundamentals of Digital Signal Processing,John Wiley, 1968

Random Processes: Filtering, Estimationand Detection, John Wiley, 2003

PHEI 2005 Chinese VersionPattern Recognition: Statistical and Neural,Currently Preparing Manuscript

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 Proposed Course

PATTERN RECOGNITION: Statistical and Neural_________________________________________________

Text: "Pattern Recognition: Statistical and Neural", Lonnie C. Ludeman  (Manuscript in progress) Instructor: Dr. Lonnie C. Ludeman, Professor General Description: Statistical pattern classification, supervised and unsupervised

learning, neural networks for pattern recognition, featureselection and extraction, clustering techniques, and syntacticalpattern recognition.

Prerequisites: Probability and random variables

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Course Topical Outline: 1. Pattern Recognition Overview 2. Statistical Pattern Recognition

Maximum A'posteriori decision rule, Minimum Probability oferror decision rule Bayes decision rule, Neyman PearsonDecision Rule Classifier performance-Risk, Probability oferror, and Receiver Operating Characteristics GeneralGaussian Case, Discriminant Functions, ClassifierPerformance Decision regions and Discriminant functions-linear, nonlinear and generalized linear

3. Training pattern recognizers The perceptron algorithm The potential function algorithm

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4. Pattern Recognition using Neural NetworksIntroduction to Neural Networks- Basics, Physical Neural NetworksArtificial Neural Networks-McColloch Pitts Model, ActivationFunctions, Continuous Perceptron Training algorithms- Delta rule forsingle layers, Back propagation for multilayer networks, Improvementsto the backpropagaton algorithm, parameter selection and guidelines,and performance evaluation

5. Other classifiers

Functional link nets, Hopfield neural nets, Hammming net classifier,MAXNET classifier, nearest neighbor rules using distance andsimilarities measures,

4. Pattern Recognition using Neural NetworksIntroduction to Neural Networks- Basics, PhysicalNeural Networks Artificial Neural Networks-McColloch Pitts Model, Activation Functions,Continuous Perceptron Training algorithms- Deltarule for single layers, Back propagation for multilayernetworks, Improvements to the backpropagaton algorithm, parameter selection and guidelines, andperformance evaluation

5. Other classifiers

Functional link nets, Hopfield neural nets,Hammming net classifier, MAXNET classifier,nearest neighbor rules using distance and similarities measures,

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6. Clustering algorithms K-means, ISODATA, Maximin, Hierarchical,Adaptive Resonance Theory,  Fuzzy Clustering

7. Syntactic Pattern Recognition 8. Feature selection 9. Data Fusion

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Current Research Interests:

Clustering of non QuantitativeArchaeological Data

Neural Networks and ClusteringEducational Software

Blind Nonlinear System Identification ofSpeech Signals

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Clustering of non quantitative data

(A) Nominal or ordinal

(B) Syntactical

(C) Fuzzy

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Pattern Recognition Educational Software

(A) LCLNET - A neural Network Simulator

(B) LCLCLUSTER - A clustering GUI

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(A) LCLNET - A neural Network Simulator

1. Train a feedforward neural network2. Test trained network using training and other non-training files3. Change parameters quickly and interactively: Layers , nodes/layer, learning and momentum parameter, nonlinearities, target values, initial conditions4. Create, store, print, and view - Data files, weight files and results5. Provide visual tracking of weight changes For trining by sample and epoch

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(A) LCLCLUSTER - A Clustering GUI

1. Data selection Interface - directory information

2. Easy Selection of Types of clustering - K-means, Hierarchical, Fuzzy, ART

3. Storage and viewing - of iterative steps and final results

4. Easy changing of parameters- Number of clusters, fuzzy index, and initial conditions

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GUI for LCLCLUSTER

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Fuzzy Clustering Results

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ln()

s(t)

x(t)

1

c sin( ct+ )

d

xln()

s(t)

x(t)

1

c sin( ct+ )

d

x

ln()

s(t)

x(t)

1

c sin( ct+ )

d

x

Non Linear Speech Model

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Estimate c

x(t)

-1

Bandpass Filter 2 c ± c/2

Estimate dc2

Bandpass Filter 3 c ± c/2

Estimate 1 + s(t)

s(t) ^

^

^ ^

^

^

Estimate c

x(t)

-1

Bandpass Filter 2 c ± c/2

Estimate dc2

Bandpass Filter 3 c ± c/2

Estimate 1 + s(t)

s(t) ^

^

^ ^

^

^

Synthesis of speech signal

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Summary ______________________

Introduced Myself

Presented Proposed Course

Discussed my Current Research Directions

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Thank you for your attention ______________________

I am happy to answer any questions.

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New Mexico

Land of Enchantment