RSS Feed - Blog|
About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.
|
About: Learning Discrete Bayesian Network Classifiers from Data Changes:Fetched by r-cran-robot on 2016-05-01 00:00:04.546512
|
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.
|
About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.
|
About: Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explore their relation is still an open problem. In this paper, we develop a half-quadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an L1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an L1-regularized error detection method by learning from uncorrupted data iteratively. We also show that the L1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings. Changes:Initial Announcement on mloss.org.
|
About: Tool aimed at helping remedy the reproducibility problem, specifically in the statistical and data wrangling aspects. Changes:Initial Announcement on mloss.org.
|
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
|
About: Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos. Changes:Initial Announcement on mloss.org.
|
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
About: An open-source Python toolbox to analyze mobile phone metadata. Changes:Initial Announcement on mloss.org.
|
|
About: The toolbox from the paper Near-optimal Experimental Design for Model Selection in Systems Biology (Busetto et al. 2013, submitted) implemented in MATLAB. Changes:Initial Announcement on mloss.org.
|
About: A Python based library for running experiments with Deep Learning and Ensembles on GPUs. Changes:Initial Announcement on mloss.org.
|
About: This MATLAB package provides the LOMO feature extraction and the XQDA metric learning algorithms proposed in our CVPR 2015 paper. It is fast, and effective for person re-identification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/. Changes:Initial Announcement on mloss.org.
|
