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Introduction Mohammad Beigi Department of Biomedical Engineering Isfahan University [email protected]

Introduction

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Introduction. Mohammad Beigi Department of Biomedical Engineering Isfahan University [email protected]. Pattern recognition and Machine Learning. Syllabus Introduction, Linear Models for classification Neural Networks (MLP, RBF, SOM, LVQ, ADALINE) - PowerPoint PPT Presentation

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Page 1: Introduction

Introduction

Mohammad BeigiDepartment of Biomedical Engineering

Isfahan [email protected]

mailto:[email protected]
Page 2: Introduction

Pattern recognition and Machine Learning

Syllabus Introduction,Linear Models for classificationNeural Networks (MLP, RBF, SOM, LVQ, ADALINE) Kernel Methods & Support Vector Machines Statistical Pattern Recognition ? (HMM,EM, Clustering and unsupervised learning ? Feature Selection and Dimension reduction ?

Page 3: Introduction

Pattern recognition and Machine Learning

TextsR. O. Duda, P. E. Hart, D. G. Stork,

Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2000.

M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

http://rii.ricoh.com/~stork/DHS.html
http://research.microsoft.com/~cmbishop/PRML/index.htm
Page 4: Introduction

• Midterm 25%• Final 40%• Computer assignments 10%• Final Programming Project 15%• Seminar 10%

Evaluation

Page 5: Introduction

Human Perception

Humans have developed highly sophisticated skills for sensing their environment and taking actions according to what they observe, e.g

Understanding spoken wordsreading handwritingdistinguishing fresh food from its smell

We would like to give similar capabilities to machines

Page 6: Introduction

What is Pattern Recognition? A pattern is an entity, vaguely defined, that could be given a name, e.g.,

fingerprint image, handwritten word, human face, speech signal, DNA sequence,

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest, make sound and reasonable decisions about the categories of the

patterns.

Page 7: Introduction

Human and Machine Perception

We are often influenced by the knowledge of how patterns are modeled and recognized in nature when we develop pattern recognition algorithms. Research on machine perception also helps us gain deeper understanding and appreciation for pattern recognition systems in nature. Yet, we also apply many techniques that are purelynumerical and do not have any correspondence in naturalsystems.

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Pattern Recognition Applications

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Pattern Recognition Applications

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Pattern Recognition Applications

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Pattern Recognition Applications

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Pattern Recognition Applications

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Pattern Recognition Applications

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Pattern Recognition Applications

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Pattern Recognition Applications

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Pattern Recognition Applications

Figure 9: Clustering of Microarray Data

Page 18: Introduction

Pattern Recognition Applications

Figure 10: Brain Control Interface

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Regression: Polynomial Curve Fitting

t is continuous

Page 36: Introduction

Sum-of-Squares Error Function

* min ( )w Arg E w Optimization Problem

Page 37: Introduction

0th Order Polynomial

Page 38: Introduction

1st Order Polynomial

Page 39: Introduction

3rd Order Polynomial

Page 40: Introduction

9th Order Polynomial

Page 41: Introduction

Over-fitting

Root-Mean-Square (RMS) Error:

Page 42: Introduction

Polynomial Coefficients

Page 43: Introduction

Data Set Size: 9th Order Polynomial

Page 44: Introduction

Data Set Size: 9th Order Polynomial

Page 45: Introduction

Regularization ;ridge regression

Penalize large coefficient values

Shrinkage: reduce the order of method

~* min ( )w Arg E w

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Regularization:

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Regularization:

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Regularization: vs.

Page 49: Introduction

Polynomial Coefficients

Optimization Problem: Finding optimum ,M

Page 50: Introduction

Classification example: Handwritten Digit Recognition

28*28 Pixel image : 784 real numbers, training set: 1{ ,.... }Nx x1x

( ),y x t {1,..,9}t

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Pattern recognition approaches

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Statistical Pattern recognition

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Statistical Pattern recognition

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Structural Pattern Recognition

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Neural Pattern Recognition


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