About: Cox models by likelihood based boosting for a single survival endpoint or competing risks Changes:Fetched by rcranrobot on 20160701 00:00:04.841105

About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly Changes:Fetched by rcranrobot on 20160701 00:00:04.963467

About: Wrapper Algorithm for AllRelevant Feature Selection Changes:Fetched by rcranrobot on 20160701 00:00:03.793481

About: Classification and Regression Training Changes:Fetched by rcranrobot on 20160701 00:00:03.996729

About: Learning Discrete Bayesian Network Classifiers from Data Changes:Fetched by rcranrobot on 20160501 00:00:04.546512

About: TBEEF, a doubly ensemble framework for recommendation and prediction problems. Changes:Included the final technical report.

About: Mining Association Rules and Frequent Itemsets Changes:Fetched by rcranrobot on 20160701 00:00:03.517840

About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplarbased agglomerative clustering, and various tools for visual analysis of clustering results. Changes:

About: Somoclu is a massively parallel implementation of selforganizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, R, and MATLAB are supported. Changes:

About: Bayesian Additive Regression Trees Changes:Fetched by rcranrobot on 20160701 00:00:03.587558

About: Gradient Boosting Changes:Fetched by rcranrobot on 20160701 00:00:03.861434

About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more Changes:This version comes with Distributed and Mobile Examples

About: KernelBased Analysis of Biological Sequences Changes:

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as crossvalidation and a plethora of score functions. Changes:This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

About: Classification, Regression and Feature Evaluation Changes:Fetched by rcranrobot on 20160701 00:00:04.771087

About: Data Sets, Functions and Examples from the Book Changes:Fetched by rcranrobot on 20160701 00:00:05.032446

About: R package implementing statistical test and post hoc tests to compare multiple algorithms in multiple problems. Changes:Initial Announcement on mloss.org.

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems Changes:

About: C5.0 Decision Trees and RuleBased Models Changes:Fetched by rcranrobot on 20160701 00:00:03.929165

About: Generalized Ridge Regression (with special advantage for p >> n cases) Changes:Fetched by rcranrobot on 20160701 00:00:03.666242
