Semi Supervised And Unsupervised Machine Learning

Semi-Supervised and Unervised Machine Learning: Novel Strategies (Repost)

Semi-Supervised and Unervised Machine Learning: Novel Strategies By Amparo Albalate, Wolfgang Minker
2011 | 244 Pages | ISBN: 1848212038 | PDF | 69 MB
Semi-Supervised and Unervised Machine Learning: Novel Strategies

Semi-Supervised and Unervised Machine Learning: Novel Strategies By Amparo Albalate, Wolfgang Minker
2011 | 244 Pages | ISBN: 1848212038 | PDF | 69 MB

Kernel Based Algorithms for Mining Huge Data Sets (Repost)  eBooks & eLearning

Posted by lenami at Nov. 21, 2010
Kernel Based Algorithms for Mining Huge Data Sets (Repost)

Kernel Based Algorithms for Mining Huge Data Sets
Publisher: Springer | ISBN: 3540316817 | edition 2006 | PDF | 260 pages | 5 mb

"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas.

Unsupervised Machine Learning in Python  eBooks & eLearning

Posted by arundhati at July 7, 2016
Unsupervised Machine Learning in Python

LazyProgrammer, "Unsupervised Machine Learning in Python: Master Data Science and Machine Learning with Cluster Analysis, Gaussian Mixture Models, and Principal Components Analysis"
2016 | ASIN: B01G1HH5T4 | 56 pages | EPUB | 1 MB
Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence [Repost]

Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence (Industrial Electronics) by Evangelia Miche Tzanakou
English | Dec 28, 1999 | ISBN: 0849322782 | 367 Pages | PDF | 16 MB

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images.
Supervised and Unsupervised Ensemble Methods and their Applications [Repost]

Oleg Okun, ‎Giorgio Valentini - Supervised and Unsupervised Ensemble Methods and their Applications
Published: 2008-04-18 | ISBN: 3540789804 | PDF | 180 pages | 3 MB
Evangelia Miche Tzanakou, Supervised and Unsupervised Pattern Recognition (Repost)

Evangelia Miche Tzanakou, Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence
ISBN: 0849322782 | edition 1999 | PDF | 367 pages | 16 mb

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods.
Supervised and Unsupervised Ensemble Methods and their Applications

Oleg Okun, Giorgio Valentini “Supervised and Unsupervised Ensemble Methods and their Applications"
Springer | 2008-05-01 | ISBN: 3540789804 | 180 pages | PDF | 3,3 Mb

Supervised and Unsupervised Pattern Recognition  eBooks & eLearning

Posted by gosiaiza at Feb. 2, 2007
Supervised and Unsupervised Pattern Recognition

Supervised and Unsupervised Pattern Recognition
392 pages | Publisher: CRC (December 28, 1999) | English | ISBN-10: 0849322782 | ISBN-13: 978-0849322785 | PDF | 16 MB
Reinforcement and Systemic Machine Learning for Decision Making [Repost]

Parag Kulkarni - Reinforcement and Systemic Machine Learning for Decision Making
Published: 2012-08-14 | ISBN: 047091999X | PDF | 312 pages | 2 MB