Machine learning : an algorithmic perspective / Stephen Marsland
Tipo de material:
TextoSeries Chapman & Hall/CRC machine learning & pattern recognitionDetalles de publicación: Boca Raton : CRC Press, 2009 Descripción: XVI, 390 p. : il. ; 25 cmISBN: 978-1-4200-6718-7Tema(s): Algoritmos computacionales | Inteligencia artificialResumen: Machine Learning: An Algorithmic Perspective" introduces this subject to computer science students and others who may not have a strong mathematical background. Focusing on algorithms and applications, the text presents three distinct sets of problems for each section: standard questions that test understanding of the material, structured programming exercises using code and data from the Internet, and suggested further investigations, often involving some basic programming. The book covers such fundamental topics as neuronal modeling, perceptron, multi-layer perceptron, classification, regression, decision trees, the naive Bayes' classifier, unsupervised learning, the self-organizing map, and genetic algorithms.Resumen: Índice: Introduction to Machine Learning. The Multi-Layer Perceptron. Classification. Unsupervised Learning. Search and Evolutionary Learning. Reinforcement Learning.
| Tipo de ítem | Biblioteca de origen | Signatura | URL | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
|---|---|---|---|---|---|---|---|
| Monografías | 01. BIBLIOTECA CAMPUS JEREZ | I-848 (Navegar estantería(Abre debajo)) | Texto completo | Prestado | 31/01/2025 | 3742894308 | |
| Monografías | 03. BIBLIOTECA INGENIERÍA PUERTO REAL | 681.3/MAR/mac (Navegar estantería(Abre debajo)) | Texto completo | Prestado | 02/05/2024 | 3743145820 |
Índice
Bibliografía
Machine Learning: An Algorithmic Perspective" introduces this subject to computer science students and others who may not have a strong mathematical background. Focusing on algorithms and applications, the text presents three distinct sets of problems for each section: standard questions that test understanding of the material, structured programming exercises using code and data from the Internet, and suggested further investigations, often involving some basic programming. The book covers such fundamental topics as neuronal modeling, perceptron, multi-layer perceptron, classification, regression, decision trees, the naive Bayes' classifier, unsupervised learning, the self-organizing map, and genetic algorithms.
Índice: Introduction to Machine Learning. The Multi-Layer Perceptron. Classification. Unsupervised Learning. Search and Evolutionary Learning. Reinforcement Learning.
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