Posted by **tanas.olesya** at March 30, 2015

English | Aug 1, 2001 | ISBN: 026208290X | 546 Pages | PDF | 30 MB

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines.

Posted by **Direktor69** at April 11, 2013

ISBN: 026208290X | edition 2001 | PDF | 578 pages | 30 mb

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections.

Posted by **tot167** at Dec. 9, 2010

The M.I.T Press | 2001 | ISBN: 026208306X | 384 pages | PDF | 2,4 MB

Posted by **Alexpal** at Jan. 31, 2007

Publisher: The MIT Press; 1st edition (December 15, 2001) | ISBN-10: 0262194759 | PDF | 36,2 Mb | 644 pages

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs— -kernels–for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Posted by **Alexpal** at Jan. 22, 2007

Publisher: The MIT Press (August 1, 2001) | ISBN-10: 026208290X | PDF | 30,6 Mb | 425 pages

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner.

Posted by **FenixN** at Dec. 5, 2016

HDRips | MP4/AVC, ~757 kb/s | 1920х1080 / 1280x720 | Duration: 13:00:13 | English: AAC, 128 kb/s (2 ch)

The R programming language has arguably become the single most important tool for computational statistics, visualization, and data science. With this Learning Path, master all the features you'll need as a data scientist, from the basics to more advanced techniques including R Graph and machine learning. You'll work your data like never before.

Posted by **FenixN** at Nov. 28, 2016

HDRips | MP4/AVC, ~222 kb/s | 1280x720 | Duration: 23:36:03 | English: AAC, 96 kb/s (2 ch) | 3.22 GB

This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and the demand for jobs is only expected to increase. Gaining skills in this field will get you one step closer to becoming a data scientist or quantitative analyst.

Posted by **Veslefrikk** at Sept. 23, 2014

Mor gan & Clay pool Publis hers | 2011 | ISBN: 1608455165 | 121 pages | PDF | 3 MB

Posted by **interes** at Sept. 16, 2014

English | 2014 | ISBN-10: 3319056417 | 254 pages | PDF | 32,5 MB

This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book from an innovative perspective that combines both neurobiology and machine learning.

Posted by **arundhati** at June 14, 2014

2014 | ISBN-10: 3319056417 | 254 pages | PDF | 33 MB