Package ‘gputools’ was removed from the CRAN repository. Formerly available versions can be obtained from the archive. Archived on 2017-12-19 as check problems were not corrected despite reminders. Please use the canonical form https://CRAN.R-project.org/package=gputools to link to this page.

A Semi-Naive Bayesian Classifier for Fast Patch Classification We show that it is possible to train a Semi-Naive Bayesian Classifier to recognize keypoints without the need for intensive preprocessing stages like fine-scale selection, dominant orientation and affine parameter estimation. We also demonstrate that this approach is superior to previous methods that use weighted averaging of Randomize

パターン認識と機械学習 - ベイズ理論による統計的予測† This is a support page for the Japanese edition of "Pattern Recognition and Machine Learning" authored by C. M. Bishop. 本書は，Christopher M. Bishop 著「Pattern Recognition and Machine Learning」の日本語版です．上下2巻の構成です． パターン認識や機械学習の各種のアルゴリズムや背後の考えについて，ベイズ理論の観点から解説した教科書です． 基礎的な線形モデルから，カーネルトリック，グラフィカルモデル，MCMCなどの発展的な話題までをバランス良く収録しています． 数式による形式的な記述だけにとどまらず，豊富なカラーの図による直観的な説明もなされています． 本

I am no longer maintaining this page, please refer to the second edition. Foreign Editions: Prentice-Hall of India (IN) has published a reprint of the book in the Eastern Economy Edition series. ISBN: 81-203-2791-8 (2005). Maschinelles Lernen (German language edition, translated by Simone Linke), Oldenbourg Verlag, ISBN-13: 978-3-486-58114-0, May 2008. 机器学习导论 (Chinese simplified character edition,

The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.

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Due to a reorganisation we are not able anymore to maintain these files. They will be removed in the near future. Software and Hardware for Data Analysis, Pattern Recognition and Image Processing The entries below provide some information on software and hardware for Data Analysis, Data Mining and Visualisation, Pattern Recognition, Machine Learning, Image Processing and related fields. Suggestion

Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued

News 09.03.2016 MDP 3.5 released! This is a bug-fix release Note that from this release MDP is in maintenance mode. 13 years after its first public release, MDP has reached full maturity and no new features are planned in the future. If you plan to do serious machine learning in Python, use sklearn. Note though that some algorithms, notably SFA and Growing Neural Gas, are only available in MDP. We

Chih-Chung Chang and Chih-Jen Lin Version 3.32 released on July 9, 2023. We fix some minor bugs. Version 3.31 released on February 28, 2023. Probabilistic outputs for one-class SVM are now supported. Version 3.25 released on April 14, 2021. Installing the Python interface through PyPI is supported > pip install -U libsvm-official The python directory is re-organized so >>> from libsvm.svmutil impo