Description: 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.
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Size: 116681 |
Author:njustyw |
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Description: 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.
Platform: |
Size: 116736 |
Author:njustyw |
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Description: 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.-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. Platform: |
Size: 6144 |
Author:来海锋 |
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Description: 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
-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
Platform: |
Size: 6144 |
Author:来海锋 |
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Description: AdaBoost, short for Adaptive Boosting, is a machine learning algorithm, formulated by Yoav Freund and Robert Schapire. It is a meta-algorithm, and can be used in conjunction with many other learning algorithms to improve their performance. Platform: |
Size: 1024 |
Author:soroosh |
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Description: Yoav Freund and Robert E. Schapire于1996提出的adaboost经典算法-a paper about adaboost purposed by Yoav Freund and Robert E. Schapire in1996. Platform: |
Size: 640000 |
Author:change |
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Description: adboost算法的一个例子。在Kearns和Valiant在1989年大作中指出了这种算法的可行性。而后，Freund在
1990年以及他和Schapire在 1994-1996年提出了boosting整个算法思路，似乎这种算法走到
了实际应用的开端。然而直到AdaBoost被viola在其人脸识别系统中运用(2001Viola和
Jones)，这种方法才彻底开始暴火.-An example adboost algorithm. Kearns and Valiant pointed at the feasibility of this method in 1989 masterpiece. Then, Freund and Schapire he made in 1990 and in 1994-1996 the idea of boosting the entire algorithm, this algorithm seems to come to the beginning of the practical application. However, until the use of AdaBoost is viola (2001Viola and Jones) in its face recognition systems, this approach was completely start a fire storm. Platform: |
Size: 89088 |
Author:liangfangfang |
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Search - Freund and Schapire