'Learning rule' presentation slideshows

Feed-Forward Neural Networks

Feed-Forward Neural Networks. ??? : ???. Content. Introduction Single-Layer Perceptron Networks Learning Rules for Single-Layer Perceptron Networks Perceptron Learning Rule Adaline Leaning Rule ?-Leaning Rule Multilayer Perceptron Back Propagation Learning algorithm.

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Learning with linear neurons

Learning with linear neurons. Adapted from Lectures by Geoffrey Hinton and Others Updated by N. Intrator, May 2007. Truth Table for Logical AND. x. y. x & y. 0. 0. 0. 1. 1. 0. 1. 1. output. inputs. Prehistory.

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Sang-Won Cho* : Ph.D. Student, KAIST Dong-Hyawn Kim: Senior Researcher, KORDI

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From Evolutionary Computation to Ensemble Learning

From Evolutionary Computation to Ensemble Learning. Xin Yao CERCIA, School of Computer Science University of Birmingham UK. Overview. Introduction (Evolutionary Computation) Multi-objective learning and ensembles Online learning with concept drifts Class imbalance learning

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Blind Source Separation by Independent Components Analysis

Blind Source Separation by Independent Components Analysis. Professor Dr. Barrie W. Jervis School of Engineering Sheffield Hallam University England B.W.Jervis@shu.ac.uk. The Problem .

650 views • 39 slides

Oscillatory Models of Hippocampal Activity and Memory

Oscillatory Models of Hippocampal Activity and Memory. Roman Borisyuk University of Plymouth, UK. In collaboration with. Frank Hoppensteadt New York University. Outline. Oscillatory model of Hippocampal Activity Memorization of sequences of events Theory of epineuronal memory.

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Outline

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For Monday

For Monday. Read chapter 22, sections 1-3 No homework. Exam 2. Take-home due Friday Clarification questions? Review questions?. Program 4. Any questions?. Real Neurons. Cells forming the basis of neural tissue Cell body Dendrites Axon Syntaptic terminals

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Data Mining with Neural Networks (HK: Chapter 7.5)

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Introduction to Neural Networks

Introduction to Neural Networks. John Paxton Montana State University Summer 2003. Chapter 2: Simple Neural Networks for Pattern Classification. x 0. 1. w 0. w 0 is the bias f(y in ) = 1 if y in >= 0 f(y in ) = 0 otherwise ARCHITECTURE. y. w 1. x 1. w n. x n. Representations.

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Self-organizing map for symbolic data

Self-organizing map for symbolic data. Presenter : Chang,Chun-Chih Authors : Miin-Shen Yang a* , Wen-Liang Hung b , De- Hua Chen a 2012, FSS. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Learning Rules 2

Learning Rules 2. Computational Neuroscience 03 Lecture 9. Reinforcement Learning. In reinforcement learning we have a stimulus s a reward r and an expected reward v.

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REFERENCES

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Derivation of a Learning Rule for Perceptrons

Neural Networks. Single Layer Perceptrons. x 1. w k 1. x 2. w k 2. ?. . . . w km. x m. Derivation of a Learning Rule for Perceptrons . Adaline (Adaptive Linear Element). Widrow [1962]. Goal:. Neural Networks. Single Layer Perceptrons. Least Mean Squares (LMS).

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Rules for Information Maximization in Spiking Neurons Using Intrinsic Plasticity

Rules for Information Maximization in Spiking Neurons Using Intrinsic Plasticity. Prashant Joshi & Jochen Triesch. Email: { joshi,triesch }@fias.uni-frankfurt.de | Web: www.fias.uni-frankfurt.de/~{joshi,triesch}. Synopsis.

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Derivation of a Learning Rule for Perceptrons

Neural Networks. Single Layer Perceptrons. x 1. w k 1. x 2. w k 2. ?. w km. x m. Derivation of a Learning Rule for Perceptrons. Adaline (Adaptive Linear Element). Widrow [1962]. Goal:. Neural Networks. Single Layer Perceptrons. Least Mean Squares (LMS).

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Question

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The Evolution of Learning Algorithms for Artificial Neural Networks

The Evolution of Learning Algorithms for Artificial Neural Networks. Published 1992 in Complex Systems by Jonathan Baxter Michael Tauraso. Genetic Algorithm on NNs. Start with a population of neural networks. Find the fitness of each for a particular task Weed out the low-fitness ones

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Model of Bee Foraging

Sensory Input. Nectar. S. Y. N. B. Action. R. WN. WY. WB. r(t). ?(t). P. Based on Predictive Hebbian Learning. Model of Bee Foraging. Iain Black.

248 views • 1 slides