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About: The bufferkdtree package is a Python library that aims at accelerating nearest neighbor computations using both k-d trees and modern many-core devices such as graphics processing units (GPUs). Changes:Initial Announcement on mloss.org.
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About: A novel method to create parallel coordinates plots on large data sets without causing a "black screen" problem. Changes:Initial Announcement on mloss.org.
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About: The Java package jLDADMM is released to provide alternative choices for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. Changes:Initial Announcement on mloss.org.
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About: R package implementing statistical test and post hoc tests to compare multiple algorithms in multiple problems. Changes:Initial Announcement on mloss.org.
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About: Simple and hopefully clean and easy to follow implementation of the Generalized Learning Vector Quantizer (GLVQ) with variants for metric adaptation (RGLVQ, GMLVQ, LiRaM). Changes:Initial Announcement on mloss.org.
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About: Distributed optimization: Support Vector Machines and LASSO regression on distributed data Changes:Initial Upload
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About: PRoNTo is freely available software and aims to facilitate the interaction between the neuroimaging and machine learning communities. The toolbox is based on pattern recognition techniques for the analysis of neuroimaging data. PRoNTo supports the analysis of all image modalities as long as they are NIfTI format files. However, only the following modalites have been tested for version 1.1: sMRI, fMRI, PET, FA (fractional anisotropy) and Beta (GLM coefficients) images. Changes:Initial Announcement on mloss.org.
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About: Stochastic neighbor embedding originally aims at the reconstruction of given distance relations in a low-dimensional Euclidean space. This can be regarded as general approach to multi-dimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. The present implementation also allows for handling dissimilarity or score-induced neighborhood topologies and makes use of quasi 2nd order gradient-based (l-)BFGS optimization. Changes:
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About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships. Changes:
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About: Matlab code for learning probabilistic SVM in the presence of uncertain labels. Changes:Added missing dataset function (thanks to Hao Wu)
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About: MATLAB toolbox for advanced Brain-Computer Interface (BCI) research. Changes:Initial Announcement on mloss.org.
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About: PSVM - Support vector classification, regression and feature extraction for non-square dyadic data, non-Mercer kernels. Changes:Initial Announcement on mloss.org.
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About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included. Changes:Spectrum LSTM package included
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About: This software is an implementation of Hidden Markov Support Vector Machines (HMSVMs). Changes:Initial Announcement on mloss.org.
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