SmileMiner是一个汇集了各种机器学习算法的纯Java函数库，它是自包含的，仅仅需要Java标准库。主要部件为：Smile，-Math，-Data，-Graph，-Interpolation，-NLP。不可多得的Java机器学习库，其文档和示例都挺赞！

• Core The core machine learning library
• Math Linear algebra, statistical distribution, hypothesis tests, random number generators, sort, special functions, various kernel, distance and rbf functions.
• Data Parsers for arff, libsvm, delimited text, sparse matrix, microarray gene expression data.
• Graph Graph algorithms on adjacency list and matrix.
• Interpolation One and two dimensional interpolation.
• NLP Natural language processing.
• Plot Swing-based data visualization library.
  

Smile implements the following major machine learning algorithms

• Classification Support Vector Machines, Decision Trees, AdaBoost, Gradient Boosting, Random Forest, Logistic Regression, Neural Networks, RBF Networks, Maximum Entropy Classifier, KNN, Naïve Bayesian, Fisher/Linear/Quadratic/Regularized Discriminant Analysis.

• Regression Support Vector Regression, Gaussian Process, Regression Trees, Gradient Boosting, Random Forest, RBF Networks, OLS, LASSO, Ridge Regression.

• Feature Selection Genetic Algorithm based Feature Selection, Ensemble Learning based Feature Selection, Signal Noise ratio, Sum Squares ratio.

• Clustering BIRCH, CLARANS, DBScan, DENCLUE, Deterministic Annealing, K-Means, X-Means, G-Means, Neural Gas, Growing Neural Gas, Hierarchical Clustering, Sequential Information Bottleneck, Self-Organizing Maps, Spectral Clustering, Minimum Entropy Clustering.

• Association Rule & Frequent Itemset Mining FP-growth mining algorithm

• Manifold learning IsoMap, LLE, Laplacian Eigenmap, PCA, Kernel PCA, Probabilistic PCA, GHA, Random Projection

• Multi-Dimensional Scaling Classical MDS, Isotonic MDS, Sammon Mapping

• Nearest Neighbor Search BK-Tree, Cover Tree, KD-Tree, LSH

• Sequence Learning Hidden Markov Model, Conditional Random Field.

• Natural Language Processing Sentence Splitter and Tokenizer, Bigram Statistical Test, Phrase Extractor, Keyword Extractor, Stemmer, POS Tagging, Relevance Ranking