'Input vectors' diaporamas de présentation

Chapter 5 Unsupervised learning

Chapter 5 Unsupervised learning. Introduction. Unsupervised learning Training samples contain only input patterns No desired output is given (teacher-less) Learn to form classes/clusters of sample patterns according to similarities among them

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BCB 444/544

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An Illustrative Example

An Illustrative Example. Apple/Banana Sorter. Prototype Vectors. McCulloch-Pitts Perceptron. Perceptron Training. How can we train a perceptron for a classification task? We try to find suitable values for the weights in such a way that the training examples are correctly classified.

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Chapter 3: Linear Methods for Regression The Elements of Statistical Learning Aaron Smalter

Chapter 3: Linear Methods for Regression The Elements of Statistical Learning Aaron Smalter. Chapter Outline. 3.1 Introduction 3.2 Linear Regression Models and Least Squares 3.3 Multiple Regression from Simple Univariate Regression 3.4 Subset Selection and Coefficient Shrinkage

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Octopus Arm Project

Octopus Arm Project. Final Presentation. Dmitry Volkinshtein & Peter Szabo. Supervised by: Yaki Engel. Contents. Project Goal The Octopus and its Arm Octopus Arm Model Reinforcement Learning On-Line GPTD Algorithm System Implementation Debug and Testing

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Chapter 4. Neural Networks Based on Competition

C_ 1. x_ 1. C_m. x_n. INPUT. CLASSIFICATION. Chapter 4. Neural Networks Based on Competition. To classify an input pattern into one of the m classes idea case: one class node has output 1, all other 0 ; often more than one class nodes have non-zero output.

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The Firm: Basics

The Firm: Basics. MICROECONOMICS Principles and Analysis Frank Cowell . October 2011 . Overview. The Firm: Basics. The setting. The environment for the basic model of the firm. Input require-ment sets. Isoquants. Returns to scale. Marginal products. The basics of production.

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Using Matlab Neural Networks Toolbox

Using Matlab Neural Networks Toolbox. Ranga Rodrigo April 5, 2014 Most of the sides are from the Matlab tutorial. Introduction. Matlab Neural Network Toolbox provides tools for designing, implementing, visualizing, and simulating neural networks.

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The Self Organizing Map (SOM) and Major League Baseball (MLB) Statistics

The Self Organizing Map (SOM) and Major League Baseball (MLB) Statistics. By: Clint Tomer MATH 3220. "Baseball, it is said, is only a game. True. And the Grand Canyon is only a hole in Arizona. Not all holes, or games, are created equal." -George F. Will. Outline.

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IE 585

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Unsupervised Learning K-means clustering The EM Algorithm Competetive Learning & SOM Networks

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Associative Networks

Associative Networks. Associative networks are able to store a set of patterns and output the one that is associated with the current input. The patterns are stored in the interconnections between neurons, similarly to how memory in our brain is assumed to work.

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Characterization Presentation Spring 2010 ASIC Tester Abo -Raya Dia - 4 th year student

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Competitive Learning Neural Networks

Competitive Learning Neural Networks. UnSupervised Learning. Competitive Learning.

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The Counterpropagation Network

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A Power Grid Analysis and Verification Tool Based on a Statistical Prediction Engine

ICECS 2010 Tools, Techniques & Circuits for Low-Power C onsumer E lectronics. A Power Grid Analysis and Verification Tool Based on a Statistical Prediction Engine. M.K. Tsiampas , D. Bountas , P. Merakos , N.E. Evmorfopoulos , S. Bantas and G.I. Stamoulis. Outline. Motivation

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FOANN: Fractional Order Artificial Neural Networks

FOANN: Fractional Order Artificial Neural Networks. YangQuan Chen Center for Self-Organizing and Intelligent Systems (CSOIS), Dept. of Electrical and Computer Engineering Utah State University E : yqchen@ieee.org; T : 1( 435)797-0148; F : 1(435)797-3054.

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????????? Pattern Recognition - Methods and Application

IPL. ??????????. ????????? Pattern Recognition - Methods and Application. ??? ??????. ????????????. ?????????. IPL. ??? ??????. ????. 1. 1. Introduction. 2. 2. Competitive Learning. 3. 3 . Self-Organizing Maps. 4. 4 . Learning Vector Quantization Networks. 5. 5 . Summary. 6.

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ANN Approach to ECG Classification

ANN Approach to ECG Classification. Joe Krachey 12/10/2001 ECE 539. Problem:. Classification of irregular heartbeats via MLP neural network. Application: Pre-screening of patients for cardiologists Monitoring/diagnosis equipment Data

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