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liquidsvm/liquidsvm
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Scala (JVM): 2.11 -
geotrellis/maml
Map Algebra Modeling Language: It's what we and whales are.
Scala (JVM): 2.11 2.12Scala.js: 0.6 -
zuinnote/spark-hadoopcryptoledger-ds
A Spark datasource for the HadoopCryptoLedger library
Scala (JVM): 2.10 2.11 2.12 -
sadikovi/spark-netflow
NetFlow data source for Spark SQL and DataFrames
Scala (JVM): 2.10 2.11 2.12