Svm Support Vector Machine

SUPPORT VECTOR MACHINE. EXAMPLES with MATLAB  eBooks & eLearning

Posted by naag at May 3, 2017
SUPPORT VECTOR MACHINE. EXAMPLES with MATLAB

SUPPORT VECTOR MACHINE. EXAMPLES with MATLAB
2017 | English | ASIN: B0711B7N6V | 243 pages | PDF + EPUB (conv) | 5.78 Mb
Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach

Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach by Jenny Terzic
English | 28 Jun. 2013 | ISBN: 3319006320 | 144 Pages | EPUB | 2.65 MB

Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is a signal classification technique which has far greater accuracy than conventional signal averaging methods.
Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach (Repost)

Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach By Jenny Terzic, Edin Terzic, Romesh Nagarajah, Muhammad Alamgir
2013 | 136 Pages | ISBN: 3319006320 | PDF | 5 MB
Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach

Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach By Jenny Terzic, Edin Terzic, Romesh Nagarajah, Muhammad Alamgir
2013 | 136 Pages | ISBN: 3319006320 | PDF | 5 MB

Support Vector Machines and Perceptrons  eBooks & eLearning

Posted by AlenMiler at Aug. 30, 2016
Support Vector Machines and Perceptrons

Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks (SpringerBriefs in Computer Science) by M.N. Murty
English | 25 Aug. 2016 | ISBN: 3319410628 | 112 Pages | PDF (True) | 1.78 MB

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation) [Repost]

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) by Bernhard Schlkopf
English | Dec. 15, 2001 | ISBN: 0262194759 | 644 Pages | PDF | 36.19 MB

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM).

Support Vector Machines: Theory and Applications (Repost)  eBooks & eLearning

Posted by step778 at Aug. 14, 2015
Support Vector Machines: Theory and Applications (Repost)

Lipo Wang, "Support Vector Machines: Theory and Applications"
2005 | pages: 434 | ISBN: 3540243887 | PDF | 15,4 mb
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing) (Repost)

Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing) by Lipo Wang
English | 2005 | ISBN: 3540243887 | 431 Pages | PDF | 15 MB

The support vector machine (SVM) has become one of the standard tools for machine learning and data mining.

Support Vector Machines Applications [Repost]  eBooks & eLearning

Posted by ChrisRedfield at Aug. 8, 2014
Support Vector Machines Applications [Repost]

Yunqian Ma, ‎Guodong Guo - Support Vector Machines Applications
Published: 2014-03-03 | ISBN: 3319022997 | PDF | 302 pages | 8 MB

Support Vector Machines Applications  eBooks & eLearning

Posted by interes at April 13, 2014
Support Vector Machines Applications

Support Vector Machines Applications by Yunqian Ma and Guodong Guo
English | 2014 | ISBN: 3319022997 | 308 pages | PDF | 8,1 MB

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.