Projects that are tagged with large datasets.


Logo scikit multilearn 0.0.4

by niedakh - February 15, 2017, 21:11:40 CET [ Project Homepage BibTeX Download ] 1458 views, 358 downloads, 3 subscriptions

About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.

Changes:

*kNN classifiers support sparse matrices properly support for the new model_selection API from scikit-learn extended graph-based label space clusteres to allow taking probability of a label occuring alone into consideration compatible with newest graphtool support the case when meka decides that an observation doesn't have any labels assigned HARAM classifier provided by Fernando Benitez from University of Konstanz predict_proba added to problem transformation classifiers ported to python 3


Logo Universal Java Matrix Package 0.3.0

by arndt - July 31, 2015, 14:23:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15063 views, 2837 downloads, 3 subscriptions

About: The Universal Java Matrix Package (UJMP) is a data processing tool for Java. Unlike JAMA and Colt, it supports multi-threading and is therefore much faster on current hardware. It does not only support matrices with double values, but instead handles every type of data as a matrix through a common interface, e.g. CSV files, Excel files, images, WAVE audio files, tables in SQL data bases, and much more.

Changes:

Updated to version 0.3.0


Logo Linear SVM with general regularization 1.0

by rflamary - October 5, 2012, 15:34:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5654 views, 1572 downloads, 1 subscription

About: This package is an implementation of a linear svm solver with a wide class of regularizations on the svm weight vector (l1, l2, mixed norm l1-lq, adaptive lasso). We provide solvers for the classical single task svm problem and for multi-task with joint feature selection or similarity promoting term.

Changes:

Initial Announcement on mloss.org.


Logo SMIDAS 1.1

by ambujtewari - August 15, 2010, 18:51:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9920 views, 2033 downloads, 1 subscription

About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression.

Changes:

Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.


Logo SCD 2.1

by ambujtewari - December 3, 2009, 22:21:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10047 views, 1968 downloads, 1 subscription

About: A (randomized) coordinate descent procedure to minimize L1 regularized loss for classification and regression purposes.

Changes:

Fixed some I/O bugs. Lines that ended with whitespace were not read correctly in the previous version.