'Initial weights' presentation slideshows

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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Neural Networks Lecture 4 Least Mean Square algorithm for Single Layer Network

Neural Networks Lecture 4 Least Mean Square algorithm for Single Layer Network. Dr. Hala Moushir Ebied. Faculty of Computers & Information Sciences Scientific Computing Department Ain Shams University. Outline. Going back to Perceptron Learning rule

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Appendix 11A: Derivation of Equation (11.6a) (11.1)

Appendix 11A: Derivation of Equation (11.6a) (11.1). By Cheng Few Lee Joseph Finnerty John Lee Alice C Lee Donald Wort. The objective function L as defined in Equation (18.3) can be rewritten: Then, following the product and chain rule, we have:

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Malware Detection Based on Malicious Behaviors Using Artificial Neural Network

Malware Detection Based on Malicious Behaviors Using Artificial Neural Network. Student: Hsun -Yi Tsai Advisor: Dr. Kuo -Chen Wang 2012/05/28. Outline. Introduction Problem Statement Related Work Design Approach Sandboxes Behaviors Proposed Algorithm Weight Training

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Theory Revision

Theory Revision. Chris Murphy. The Problem. Sometimes we: Have theories for existing data that do not match new data Do not want to repeat learning every time we update data Believe that our rule learners could perform much better if given basic theories to build off of.

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Neural Networks And Its Applications

Neural Networks And Its Applications. By Dr. Surya Chitra. OUTLINE. Introduction & Software Basic Neural Network & Processing Software Exercise Problem/Project Complementary Technologies Genetic Algorithms Fuzzy Logic Examples of Applications Manufacturing R&D Sales & Marketing

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RISK ASSESSMENT OF SEWER CONDITION USING ARTIFICIAL INTELLIGENCE TOOLS Application to the SANEST sewer system

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Neural Network Implementation of Poker AI

Neural Network Implementation of Poker AI. Christopher Kramer. Outline of Information. The Challenge Application, problem to be solved, motivation Why create a poker machine with ANNE? The Flop The hypothesis Can a Poker AI run using only an ANNE? The Turn The approach and methodology

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Self-Organizing Maps

Self-Organizing Maps. Corby Ziesman March 21, 2007. Overview. A Self-Organizing Map (SOM) is a way to represent higher dimensional data in an usually 2-D or 3-D manner, such that similar data is grouped together. It runs unsupervised and performs the grouping on its own.

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Self-Organizing Maps

Self-Organizing Maps. Corby Ziesman March 21, 2007. Overview. A Self-Organizing Map (SOM) is a way to represent higher dimensional data in an usually 2-D or 3-D manner, such that similar data is grouped together. It runs unsupervised and performs the grouping on its own.

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Differential Photometry With Variable Reference Stars

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Examples of Ensemble Methods

Examples of Ensemble Methods. How to generate an ensemble of classifiers? Bagging Boosting. Bagging. Sampling with replacement Build a classifier on each bootstrap sample Each example has probability 1/N being selected

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Example, BP learning function XOR

0. 0. 1. 1. p j. o. 2. 2. W (2,1). W (1,0). Example, BP learning function XOR. Initial weights W(0) Learning rate = 0.2 Node function: hyperbolic tangent. Training samples (bipolar) Network: 2-2-1 with thresholds (fixed output 1). MSE reduction: every 10 epochs.

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