In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

This paper presents the top 10 data mining algorithms identified by the IEEE International
Conference on Data Mining (ICDM) in December 2006: C4. 5, k-Means, SVM, Apriori, EM …

摘要AdaBoost 是最优秀的Boosting 算法之一, 有着坚实的理论基础, 在实践中得到了很好的
推广和应用. 算法能够将比随机猜测略好的弱分类器提升为分类精度高的强分类器 …

Recent years have witnessed a proliferation of large-scale knowledge bases, including
Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase …

Boosting Boosting—(is an approach to machine learning based on the idea of creating a
highly accurate prediction rule by combining many relatively weak and inaccurate rules. The …

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach,
Ensemble Methods: Foundations and Algorithms shows how these accurate methods are …

As shown in recent research, deep neural networks can perfectly fit randomly labeled data,
but with very poor accuracy on held out data. This phenomenon indicates that loss functions …

AdaBoost is one of the most excellent Boosting algorithms. It has a solid theoretical basis
and has made great success in practical applications. AdaBoost can boost a weak learning …

Background: The concept of boosting emerged from the field of machine learning. The basic
idea is to boost the accuracy of a weak classifying tool by combining various instances into a …

It is often desirable to be able to recognize when inputs to a recognition function learned in a
supervised manner correspond to classes unseen at training time. With this ability, new …