August 2, 2019

This time around on SD Times GitHub Project of the

first_imgThis time around on SD Times GitHub Project of the Week, we are shining the spotlight on SmileMiner, which stands for Statistical Machine Intelligence and Learning Engine. Created by Haifeng Li, a chief data scientist at ADP, SmileMiner is a comprehensive library and engine of advanced machine-learning algorithms.“SMILE is self contained and requires only the standard Java library,” he wrote on his blog. “With advanced data structures and learning algorithms, SMILE achieves the state of the art of performance,” Li wrote on his blog.According to Li, the major components of SmileMiner include:A core machine-learning libraryMathematical functionsGraph algorithmsOne- and two-dimensional interpolationA Swing-based data visualization library, which requires SwingX library for JXTableIn addition, SmileMiner implements major machine-learning algorithms such as:Classification, including support vector machines, decision trees, AdaBoost, gradient boosting, logistic regression and neural networksRegression, including support vector regression, regression processes, regression trees, gradient boosting, random forest, and ridge regressionFeature selection, including genetic algorithm-based feature selection, signal noise ratio, and sum squares ratioClustering, including deterministic annealing, growing neural gas, hierarchical clustering and self-organizing mapsAssociation rule and frequent item set mining, including the FP-Growth mining algorithmManifold learning, including Laplacian Eigenmap, PCA, kernel PCA, probabilistic PBA, and random projectionNearest neighbor search, including BK-tree, cover tree, KD-tree and LSHSequence learning, including the hidden Markov modelMore information is available here.last_img