'Perceptron' diaporamas de présentation

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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Artificial neural networks

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Linear SVM

Linear SVM. From perceptron to kernel methods. Following Scholkopf, see SVM site. Course website: http://horn.tau.ac.il/course06.html. Support Vector Clustering. Given points x in data space, define images in Hilbert space.

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Chapter 4: Artificial Neural Networks

Chapter 4: Artificial Neural Networks. Artificial neural network(ANN) General, practical method for learning real-valued, discrete-valued, vector-valued functions from examples BACPROPAGATION ???? Use gradient descent to tune network parameters to best fit a training set of input-output pairs

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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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Wed June 12

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Artificial Neural Networks

Artificial Neural Networks. Dr. Lahouari Ghouti Information & Computer Science Department. Single-Layer Perceptron (SLP). Architecture. We consider the following architecture : feed-forward neural network with one layer

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Neural Networks in Electrical Engineering Prof. Howard Silver

Neural Networks in Electrical Engineering Prof. Howard Silver School of Computer Sciences and Engineering Fairleigh Dickinson University Teaneck, New Jersey. Axon from Another Neuron. Dendrite of Another Neuron. Dendrite. Soma. Synaptic Gap. Axon. Synaptic Gap.

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Merging Path, Global and Local Indexing in Perceptron Branch Prediction

Merging Path, Global and Local Indexing in Perceptron Branch Prediction. David Tarjan. Published in:. An Ahead Pipelined Alloyed Perceptron with Single Cycle Access Time D. Tarjan, K. Skadron and M. Stan Workshop on Complexity Effective Design (WCED), June 2004

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Structured Perceptron

Structured Perceptron. Alice Lai and Shi Zhi. Presentation Outline. Introduction to Structured Perceptron ILP-CRF Model Averaged Perceptron Latent Variable Perceptron. Motivation. An algorithm to learn weights for structured prediction

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Fun with Hyperplanes: Perceptrons, SVMs, and Friends

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SNoW: Sparse Network of Winnows

SNoW: Sparse Network of Winnows. Presented by Nick Rizzolo. Introduction. Multi-class classification Infinite attribute domain User configurable linear threshold unit networks Variety of update rules, algorithmic extensions. Outline. The Basic System Training Algorithmic Extensions

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Question

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Soft computing

Soft computing. Lecture 6 Introduction to neural networks. Disadvantages of fuzzy systems. Difficulties of formalized of fuzzy sets and linguistic variables Its description is subjective, Its description depend on context, features of situation and inference,

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Perceptrons

Perceptrons. T he perceptron was created by Rosenblatt . S ingle-layer network whose weights and biases could be trained to produce a correct target vector when presented with the corresponding input vector. Perceptrons.

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CPSC 327

CPSC 327. Connectionist Slides. PDP with Hidden Layer. Delta. The World of Sharks and Jets. Rosenblatt's Perceptron. A Perceptron Implementation. Perceptron Data. Linearly Separable Data.

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Machine Learning 15-10-07

Machine Learning 15-10-07. Perceptron (L.T.U) ?. Find w such that for all i w i x i > 0 if d i =1 < 0 if d i =-1 OR Sign (w i x i ) = d i OR d i (w i x i > 0) ?. Soft Threshold (L.T.U) ?. Linear Regression.

168 views • 7 slides