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.
标签： meta-learning classifiers combining Boosting
上传时间： 2016-01-29
上传用户：songnanhua
acm防御导弹问题详细解答，所用方法为dp 和greedy algorithm，完整的解决了这个问题。
上传时间： 2016-07-07
上传用户：sdq_123
The combinatorial core of the OVSF code assignment problem that arises in UMTS is to assign some nodes of a complete binary tree of height h (the code tree) to n simultaneous connections, such that no two assigned nodes (codes) are on the same root-to-leaf path. Each connection requires a code on a specified level. The code can change over time as long as it is still on the same level. We consider the one-step code assignment problem: Given an assignment, move the minimum number of codes to serve a new request. Minn and Siu proposed the so-called DCAalgorithm to solve the problem optimally. We show that DCA does not always return an optimal solution, and that the problem is NP-hard. We give an exact nO(h)-time algorithm, and a polynomial time greedy algorithm that achieves approximation ratio Θ(h). Finally, we consider the online code assignment problem for which we derive several results
标签： combinatorial assignment problem arises
上传时间： 2014-01-19
上传用户：BIBI
