Combining Pattern Classifiers: Methods and Algorithms by Ludmila I. Kuncheva

By Ludmila I. Kuncheva

A unified, coherent remedy of present classifier ensemble tools, from basics of trend reputation to ensemble function choice, now in its moment edition

The paintings and technology of mixing trend classifiers has flourished right into a prolific self-discipline because the first version of Combining development Classifiers used to be released in 2004. Dr. Kuncheva has plucked from the wealthy panorama of modern classifier ensemble literature the subjects, tools, and algorithms that would consultant the reader towards a deeper figuring out of the basics, layout, and purposes of classifier ensemble methods.

Thoroughly up-to-date, with MATLAB® code and perform facts units all through, Combining development Classifiers includes:

  • Coverage of Bayes determination idea and experimental comparability of classifiers
  • Essential ensemble tools similar to Bagging, Random woodland, AdaBoost, Random subspace, Rotation woodland, Random oracle, and blunder Correcting Output Code, between others
  • Chapters on classifier choice, range, and ensemble characteristic selection

With enterprise grounding within the basics of trend reputation, and that includes greater than a hundred and forty illustrations, Combining development Classifiers, moment Edition is a necessary reference for postgraduate scholars, researchers, and practitioners in computing and engineering.

Show description

Read Online or Download Combining Pattern Classifiers: Methods and Algorithms PDF

Similar imaging systems books

Morphological Image Analysis: Principles and Applications

From reports of the 1st variation: "This is a scholarly travel de strength during the international of morphological picture research […]. i like to recommend this publication unreservedly because the most sensible one i've got encountered in this specific subject […]" BMVA information

Handbook of Optical and Laser Scanning, Second Edition (Optical Science and Engineering)

From its preliminary booklet titled Laser Beam Scanning in 1985 to guide of Optical and Laser Scanning, now in its moment variation, this reference has saved pros and scholars on the leading edge of optical scanning expertise. rigorously and meticulously up to date in every one new release, the ebook remains to be the main finished scanning source out there.

2D and 3D Image Analysis by Moments

Offers contemporary major and quick improvement within the box of second and 3D photo research second and 3D picture research through Moments, is a distinct compendium of moment-based photo research such as conventional tools and likewise displays the newest improvement of the sector. The booklet offers a survey of 2nd and 3D second invariants with appreciate to similarity and affine spatial adjustments and to photograph blurring and smoothing via a variety of filters.

Additional info for Combining Pattern Classifiers: Methods and Algorithms

Sample text

In an M × K-fold cross validation, the data is split M times into K folds, and a cross-validation is performed on each such split. This procedure results in M × K estimates of P̂ D , whose average produces the desired estimate. A 10 × 10-fold cross-validation is a typical choice of such a protocol. r Leave-one-out. This is the cross-validation protocol where K = N, that is, one object is left aside, the classifier is trained on the remaining N − 1 objects, and the left out object is classified.

We are back to using the good old hold-out method, first because the others might be too time-consuming, and second, because the amount of data might be so excessive that small parts of it will suffice for training and testing. For example, consider a data set obtained from retail analysis, which involves hundreds of thousands of transactions. Using an estimate of the error over, say, 10,000 data points, can conveniently shrink the confidence interval and make the estimate sufficiently reliable.

For values N > 25, the following statistic is approximately normally distributed [89]: z= √ 6 Function signrank this test. T − 14 N(N + 1) 1 N(N 24 . + 1)(2N + 1) from the Statistics Toolbox of MATLAB can be used to calculate the p-value for EXPERIMENTAL COMPARISON OF CLASSIFIERS 27 The sign test. A simpler but less powerful alternative to this test is the sign test. This time we do not take into account the magnitude of the differences, only their sign. By doing so, we further avoid the problem of noncommensurable errors or differences thereof.

Download PDF sample

Rated 4.25 of 5 – based on 14 votes