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About: Epistatic miniarray profiles (E-MAPs) are a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used - a simple weighted nearest neighbors, and a local least squares based regression. Changes:Initial Announcement on mloss.org.
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About: A community detection method based on constrained fractional set programming (CFSP). Changes:Initial Announcement on mloss.org.
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About: Java package for calculating Entropy for Machine Learning Applications. It has implemented several methods of handling missing values. So it can be used as a lab for examining missing values. Changes:Discretizing numerical values is added to calculate mode of values and fractional replacement of missing ones. class diagram is on the web http://profs.basu.ac.ir/bathaeian/free_space/jemla.rar
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About: This Matlab package implements a method for learning a choquistic regression model (represented by a corresponding Moebius transform of the underlying fuzzy measure), using the maximum likelihood approach proposed in [2], eqquiped by sigmoid normalization, see [1]. Changes:Initial Announcement on mloss.org.
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About: A MATLAB toolkit for performing generalized regression with equality/inequality constraints on the function value/gradient. Changes:Initial Announcement on mloss.org.
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About: OpenPR-NBEM is an C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. OpenPR-NBEM uses the multinomial event model for representation. The maximum likelihood estimate is used for supervised learning, and the expectation-maximization estimate is used for semi-supervised and un-supervised learning. Changes:Initial Announcement on mloss.org.
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About: Collection of algorithms for Gaussian Processes. Regression, Classification, Multi task, Multi output, Hierarchical, Sparse Changes:Initial Announcement on mloss.org.
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About: AIDE (Automata Identification Engine) is a free open source tool for automata inference algorithms developed in C# .Net. Changes:Initial Announcement on mloss.org.
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About: This toolbox implements a novel visualization technique called Sectors on Sectors (SonS), and a extended version called Multidimensional Sectors on Sectors (MDSonS), for improving the interpretation of several data mining algorithms. The MDSonS method makes use of Multidimensional Scaling (MDS) to solve the main drawback of the previous method, namely, the lack of representing distances between pairs of clusters. These methods have been applied for visualizing the results of hierarchical clustering, Growing Hierarchical Self-Organizing Maps (GHSOM), classification trees and several manifolds. These methods make possible to extract all the existing relationships among centroids’ attributes at any hierarchy level. Changes:Initial Announcement on mloss.org.
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About: ARTOS can be used to quickly learn models for visual object detection without having to collect a set of samples manually. To make this possible, it uses ImageNet, a large image database with more than 20,000 categories. Changes:Initial Announcement on mloss.org.
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About: Bayesian Logic (BLOG) is a probabilistic modeling language. It is designed for representing relations and uncertainties among real world objects. Changes:Initial Announcement on mloss.org.
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About: RLScore - regularized least-squares machine learning algorithms package Changes:Initial Announcement on mloss.org.
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About: Software to perform isoline retrieval, retrieve isolines of an atmospheric parameter from a nadir-looking satellite. Changes:Added screenshot, keywords
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About: Classification and Regression Training LSF Style: Augment some caret functions for parallel processing Changes:Initial Announcement on mloss.org.
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About: DDN learns and visualize differential dependency networks from condition-specific data. Changes:Initial Announcement on mloss.org.
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About: Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.) Changes:Initial Announcement on mloss.org.
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About: Mean-Shift (MS) is a powerful non-parametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. For the purpose of algorithm speedup, an agglomerative MS clustering method called Agglo-MS was developed, along with its mode-seeking ability and convergence property analysis. The method is built upon an iterative query set compression mechanism which is motivated by the quadratic bounding optimization nature of MS. The whole framework can be efficiently implemented in linear running time complexity. Changes:Initial Announcement on mloss.org.
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About: DRVQ is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. Changes:Initial Announcement on mloss.org.
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About: A Sortware for All Pairs Similarity Search Changes:Initial Announcement on mloss.org.
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