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Machine Learning Group at National Taiwan University Contributors Introduction LIBFFM is an open source tool for field-aware factorization machines (FFM). For the formulation of FFM, please see this paper. It has been used to win the top-3 in recent click-through rate prediction competitions (Criteo, Avazu, Outbrain, and RecSys 2015). It supports l2-regularized logistic loss Main features include

Distributed LIBLINEAR: Libraries for Large-scale Linear Classification on Distributed Environments Machine Learning Group at National Taiwan University Contributors We now support MPI LIBLINEAR (released in July, 2023 and based on LIBLINEAR 2.47) and Spark LIBLINEAR (released in August, 2015 and based on LIBLINEAR 1.96). The development of distributed LIBLINEAR is still in its early stage. Your co

LIBMF: A Matrix-factorization Library for Recommender Systems Machine Learning Group at National Taiwan University Version 2.01 released on February 20, 2016. LIBMF can solve more formulations than its previous versions and do disk-level training. Please check [3] for the details. Introduction LIBMF is an open source tool for approximating an incomplete matrix using the product of two matrices in

LibShortText: A Library for Short-text Classification and Analysis Machine Learning Group at National Taiwan University Contributors Version 1.1 released on September 10, 2013. Introduction LibShortText is an open source tool for short-text classification and analysis. It can handle the classification of, for example, titles, questions, sentences, and short messages. Main features of LibShortText

LIBSVM Data: Classification (Multi-class) This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format. For some sets raw materials (e.g., original texts) are also available. These data sets are from UCI, Statlog, StatLib and other collections. We thank their efforts. For most sets, we linearly scale each attribute to [-1,1] or [0,1]. The testing dat

This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format. For some sets raw materials (e.g., original texts) are also available. These data sets are from UCI, Statlog, StatLib and other collections. We thank their efforts. For most sets, we linearly scale each attribute to [-1,1] or [0,1]. The testing data (if provided) is adjusted accordingly. S

last modified : Wed, 1 Mar 2023 08:59:19 GMT All Questions(84) Q01:_Some_sample_uses_of_libsvm(2) Q02:_Installation_and_running_the_program(13) Q03:_Data_preparation(7) Q04:_Training_and_prediction(29) Q05:_Cross_validation_and_parameter_selection(9) Q06:_Probability_outputs(3) Q07:_Graphic_interface(3) Q08:_Java_version_of_libsvm(4) Q09:_Python_interface(1) Q10:_MATLAB_OCTAVE_interface(13) Some c

Chih-Jen Lin Introduction The code can be directly run on GPU. See example below. (added on April 19, 2020) This tool solves NMF by alternative non-negative least squares using projected gradients. It converges faster than the popular multiplicative update approach. Details and comparisons are in the following paper: C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neur

This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format. For some sets raw materials (e.g., original texts) are also available. These data sets are from UCI, Statlog, StatLib and other collections. We thank their efforts. For most sets, we linearly scale each attribute to [-1,1] or [0,1]. The testing data (if provided) is adjusted accordingly. S

Machine Learning Group at National Taiwan University Contributors Version 2.47 released on July 9, 2023. We fix some minor bugs. Version 2.43 released on February 25, 2021. Installing the Python interface through PyPI is supported > pip install -U liblinear-official The python directory is re-organized so >>> from liblinear.liblinearutil import * instead of >>> from liblinearutil import * should b

Last modified: This page provides some miscellaneous tools based on LIBSVM (and LIBLINEAR). Roughly they include Things not general enough to be included in LIBSVM Research codes used in some our past papers Some data sets in LIBSVM formats They will be less maintained comparing to the main LIBSVM package. However, comments are still welcome. Please properly cite our work if you find them useful.

Chih-Wei Hsu and Chih-Jen Lin BSVM 2.09 released on December 15, 2018. The two multi-class implementations included after BSVM 2.01 are two of the five methods compared in the following paper: A comparison on methods for multi-class support vector machines . (However, there is one difference: In the paper kernel caches stored numbers in double precision but in this release cached values are in sin

Chih-Chung Chang and Chih-Jen Lin Version 3.32 released on July 9, 2023. We fix some minor bugs. Version 3.31 released on February 28, 2023. Probabilistic outputs for one-class SVM are now supported. Version 3.25 released on April 14, 2021. Installing the Python interface through PyPI is supported > pip install -U libsvm-official The python directory is re-organized so >>> from libsvm.svmutil impo