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About: Learning Discrete Bayesian Network Classifiers from Data Changes:Fetched by r-cran-robot on 2016-05-01 00:00:04.546512
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About: Software for graph similarity search for massive graph databases Changes:Initial Announcement on mloss.org.
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About: Tool aimed at helping remedy the reproducibility problem, specifically in the statistical and data wrangling aspects. Changes:Initial Announcement on mloss.org.
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About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.
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About: MIPS is a software library for state-of-the-art graph mining algorithms. The library is platform independent, written in C++(03), and aims at implementing generic and efficient graph mining algorithms. Changes:description update
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About: This evaluation toolkit provides a unified framework for evaluating bag-of-words based encoding methods over several standard image classification datasets. Changes:Initial Announcement on mloss.org.
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About: HierLearning is a C++11 implementation of a general-purpose, multi-agent, hierarchical reinforcement learning system for sequential decision problems. Changes:Initial Announcement on mloss.org.
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About: Text-to-Speech (TTS) is a kind of speech processing technology that converts text into speech. It involves phonetics, linguistics, digital signal processing technology, computer technology, multimedia technology, and other technologies. It is a frontier technology in Chinese information processing field. With TTS technology, any text used to be read by eyes can also be listened by ears. Changes:Initial Announcement on mloss.org.
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About: Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos. Changes:Initial Announcement on mloss.org.
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About: DIANNE is a modular software framework for designing, training and evaluating artificial neural networks on heterogeneous, distributed infrastructure . It is built on top of OSGi and AIOLOS and can transparently deploy and redeploy (parts of) a neural network on multiple machines, as well as scale up training on a compute cluster. Changes:Initial Announcement on mloss.org.
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About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.
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About: This provide a semi-supervised learning method based co-training for RGB-D object recognition. Besides, we evaluate four state-of-the-art feature learing method under the semi-supervised learning framework. Changes:Initial Announcement on mloss.org.
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About: This package includes implementations of the CCM, DMV and DMV+CCM parsers from Klein and Manning (2004), and code for testing them with the WSJ, Negra and Cast3LB corpuses (English, German and Spanish respectively). A detailed description of the parsers can be found in Klein (2005). Changes:Initial Announcement on mloss.org.
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About: Hype is a proof-of-concept deep learning library, where you can perform optimization on compositional machine learning systems of many components, even when such components themselves internally perform optimization. Changes:Initial Announcement on mloss.org.
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About: An open-source Python toolbox to analyze mobile phone metadata. Changes:Initial Announcement on mloss.org.
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About: Easy differential privacy Changes:Initial Announcement on mloss.org.
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About: A Python based library for running experiments with Deep Learning and Ensembles on GPUs. Changes:Initial Announcement on mloss.org.
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About: a tool for marking samples in images for database building, also including algorithm of LBP,HOG,and classifiers of SVM (six kernels), adaboost,BP and convolutional networks, extreme learning machine. Changes:Initial Announcement on mloss.org.
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About: Jmlp is a java platform for both of the machine learning experiments and application. I have tested it on the window platform. But it should be applicable in the linux platform due to the cross-platform of Java language. It contains the classical classification algorithm (Discrete AdaBoost.MH, Real AdaBoost.MH, SVM, KNN, MCE,MLP,NB) and feature reduction(KPCA,PCA,Whiten) etc. Changes:Initial Announcement on mloss.org.
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