K-Nearest Neighbors 该算法存储所有的训练样本(已知标签),然后通过分析新给的样本(标签未知)与已知标签的训练样本的相似度,选出其中的K个最相似的训练样本进行投票得到新样本的标签,并计算加权和等. 该方法有时被称为是"learning by example",因为他总是根据新样本的特征向量与已知标签的样本特征向量的相似度来判断新样本的类别. CvKNearest class CvKNearest : public CvStatModel 该类实现了 K-Nearest
原文地址:http://www.demnag.com/b/java-machine-learning-tools-libraries-cm570/?ref=dzone This is a list of 25 Java Machine learning tools & libraries. Weka has a collection of machine learning algorithms for data mining tasks. The algorithms can either be
1 Unsupervised Learning 1.1 k-means clustering algorithm 1.1.1 算法思想 1.1.2 k-means的不足之处 1.1.3 如何选择K值 1.1.4 Spark MLlib 实现 k-means 算法 1.2 Mixture of Gaussians and the EM algorithm 1.3 The EM Algorithm 1.4 Principal Components
Introduction Optimization is always the ultimate goal whether you are dealing with a real life problem or building a software product. I, as a computer science student, always fiddled with optimizing my code to the extent that I could brag about its
A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning by Jason Brownlee on September 9, 2016 in XGBoost 0 0 0 0 Gradient boosting is one of the most powerful techniques for building predictive models. In this post you will dis
声明:本博客整理自博友@zhouyong计算广告与机器学习-技术共享平台,尊重原创,欢迎感兴趣的博友查看原文. 写在前面 记得在<Pattern Recognition And Machine Learning>一书中的开头有讲到:“概率论.决策论.信息论3个重要工具贯穿着<PRML>整本书,虽然看起来令人生畏…”.确实如此,其实这3大理论在机器学习的每一种技法中,或多或少都会出现其身影(不局限在概率模型). <PRML>书中原话:”This chapter also