Pattern Recognition and Machine Learning for Self-Study I
portes grátis
Pattern Recognition and Machine Learning for Self-Study I
Supervised Learning
Ueda, Naonori; Maeda, Eisaku; Ishii, Kenichiro; Murase, Hiroshi
Springer Verlag, Singapore
04/2026
461
Dura
Inglês
9789819514779
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Part I Linear Classification.- Chapter 1 Basic Concepts of Pattern Recognition.- Chapter 2 Linear Discriminant Functions and their Learning.- Chapter 3 Learning based on Minimum Squared Error Criterion.- Chapter 4 Classifier Design.-Chapter 5 Feature Evaluation and Bayes Error.- Chapter 6 Transformation of Feature Space.- Part II Nonlinear Classification.- Chapter 7 Subspace Method.- Chapter 8 Generalized Linear Discriminant Functions.- Chapter 9 Potential Function Method.- Chapter 10 Support Vector Machines. Chapter 11 Kernel Methods.- Chapter 12 Neural Networks.- Part III Bayesian Unified Framework.- Chapter 13 Convolutional Neural Networks.- Chapter 14 Generalization of Learning Algorithms.- Chapter 15 Learning Algorithms and Bayes Decision Rule.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Bayes decision rule;minimum squared error learning;support vector machine;kernel function;convolutional neural network;potential function;subspace method;primal and dual representations of perceptrons;generalized linear discriminant function;deep learning;nearest neighbor rule;Phi function;decision boundary;parametric and nonparametric learning;margin;Fisher's method;Karhunen-Loeve expansion;feature vector;prototype
Part I Linear Classification.- Chapter 1 Basic Concepts of Pattern Recognition.- Chapter 2 Linear Discriminant Functions and their Learning.- Chapter 3 Learning based on Minimum Squared Error Criterion.- Chapter 4 Classifier Design.-Chapter 5 Feature Evaluation and Bayes Error.- Chapter 6 Transformation of Feature Space.- Part II Nonlinear Classification.- Chapter 7 Subspace Method.- Chapter 8 Generalized Linear Discriminant Functions.- Chapter 9 Potential Function Method.- Chapter 10 Support Vector Machines. Chapter 11 Kernel Methods.- Chapter 12 Neural Networks.- Part III Bayesian Unified Framework.- Chapter 13 Convolutional Neural Networks.- Chapter 14 Generalization of Learning Algorithms.- Chapter 15 Learning Algorithms and Bayes Decision Rule.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Bayes decision rule;minimum squared error learning;support vector machine;kernel function;convolutional neural network;potential function;subspace method;primal and dual representations of perceptrons;generalized linear discriminant function;deep learning;nearest neighbor rule;Phi function;decision boundary;parametric and nonparametric learning;margin;Fisher's method;Karhunen-Loeve expansion;feature vector;prototype
