weka machine learning

What is integrated learning, in a word, heads the top of Zhuge Liang. In the performance of classification, multiple weak classifier combinations become strong classifiers. In a word, it is assumed that there are some differences between the weak classifiers (such as different algorithms, or different parameters of the same algorithm), which results in different classification decision boundaries, which means that they make different mistakes when ma

http://blog.csdn.net/pipisorry/article/details/44904649Machine learning machines Learning-andrew NG Courses Study notesLarge Scale machines Learning large machine learningLearning with Large datasets Big Data Set LearningStochastic Gradient descent random gradient descentMini-batch Gradient descent mini batch processin

Students in the field of machine learning know that there is a universal theorem in machine learning: There is no free lunch (no lunch). The simple and understandable explanation for it is this: 1, an algorithm (algorithm a) on a specific data set than the performance of another algorithm (algorithm B) at the same ti

of Weka. Unfortunately the content is general. The theoretical part is too thin, and the practical part is very detached from the reality. DM has a lot of introductory books, this one should not be looked at. If you want to learn about Weka, it's good to read the documentation. The second edition has been out, have not read, not clear.In terms of information retrieval,Du Lei 's classmates re-recommended:

temporal tagger-sutime is a library that recognizes and standardizes time expressions. Stanford spied-usage mode on the seed set, learning character entities from unlabeled text in iterative mode Stanford topic modeling toolbox-a topic modeling tool for social scientists and other people who want to analyze datasets. Twitter text java-implemented Twitter Text Processing Library Mallet-Java-based statistical natural language processing, document c

Stanford topic modeling toolbox-a topic modeling tool for social scientists and other people who want to analyze datasets. Twitter text java-implemented Twitter Text Processing Library Mallet-Java-based statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning text application packages. Opennlp-

We will learn how to systematically improve machine learning algorithms, tell you when the algorithm is not doing well, and describe how to ' debug ' your learning algorithms and improve their performance "best practices". To optimize machine learning algorithms, you need to

Https://yq.aliyun.com/articles/278837?utm_source=tuicoolutm_medium=referral Summary: Are you a Java programmer who wants to start or learn about machine learning? Using machine learning to write programs is the best way to learn. You can write the algorithm from scratch, but with the existing open source library, you c

deep understanding of machine learning: Learning Notes from principles to algorithms-1th week 02 easy to get started Deep understanding of machine learning from principle to algorithmic learning notes-1th week 02 Easy to get star