'Training vectors' diaporamas de présentation

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Liao Huilian SZU TI-DSPs LAB Aug 27, 2007

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Liao Huilian SZU TI-DSPs LAB Aug 27, 2007

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An Introduction to Support Vector Machine (SVM)

An Introduction to Support Vector Machine (SVM). Presenter : Ahey Date : 2007/07/20 The slides are based on lecture notes of Prof. ??? and Daniel Yeung. Outline. Background Linear Separable SVM Lagrange Multiplier Method Karush-Kuhn-Tucker (KKT) Conditions Non-linear SVM: Kernel

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Applications of Risk Minimization to Speech Recognition

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The Counterpropagation Network

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Recognition by Appearance

Recognition by Appearance. Appearance-based recognition is a competing paradigm to features and alignment. No features are extracted! Images are represented by basis functions (eigenvectors) and their coefficients . Matching is performed on this compressed image representation.

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3.4.1 Elements of Vector Quantization Implementation

3.4.1 Elements of Vector Quantization Implementation. 3.4.4 Clustering the Training Vectors. 3.4.1 Elements of Vector Quantization Implementation. Human Auditory Structure. 3.5 AUDITORY-BASED SPECTRAL ANALYSIS MODELS. 3.5.1 The EIH Model. 3.5.1 The EIH Model.

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