Boosting is a Meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標(biāo)簽： Meta-learning classifiers combining Boosting
上傳時(shí)間： 2016-01-30
上傳用戶：songnanhua
AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.
標(biāo)簽： well-known algorithm AdaBoost Adaptive
上傳時(shí)間： 2014-01-15
上傳用戶：qiaoyue
AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files
標(biāo)簽： well-known algorithm AdaBoost Adaptive
上傳時(shí)間： 2013-12-31
上傳用戶：jiahao131
Learning Wireless Java
標(biāo)簽： Learning Wireless Java
上傳時(shí)間： 2014-11-23
上傳用戶：yxgi5
machine learning
上傳時(shí)間： 2015-02-05
上傳用戶：來茴
Learning.Python.2nd.Edition
標(biāo)簽： Learning Edition Python nd
上傳時(shí)間： 2014-11-24
上傳用戶：tfyt
Learning Standard C++ as a New Language
標(biāo)簽： Learning Language Standard New
上傳時(shí)間： 2015-02-25
上傳用戶：libenshu01
USBFX2LK WDM drive for OSR s USB FX2 Learning Kit
標(biāo)簽： USBFX2LK Learning drive OSR
上傳時(shí)間： 2015-03-11
上傳用戶：huannan88
Locally weighted polynomial regression LWPR is a popular instance based al gorithm for learning continuous non linear mappings For more than two or three in puts and for more than a few thousand dat apoints the computational expense of pre dictions is daunting We discuss drawbacks with previous approaches to dealing with this problem
標(biāo)簽： polynomial regression weighted instance
上傳時(shí)間： 2013-11-28
上傳用戶：sunjet
Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP. Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
標(biāo)簽： Description supervised algorithm S-ISOMAP
上傳時(shí)間： 2015-04-10
上傳用戶：wfeel
蟲蟲下載站版權(quán)所有 京ICP備2021023401號(hào)-1
