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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: testing mloss.org Changes:Initial Announcement on mloss.org.
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About: Local high-order regularization for semi-supervised learning Changes:Initial Announcement on mloss.org.
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About: This program is a C++ implementation of Linear Discriminant Function Classifier. Discriminant functions such as perceptron criterion, cross entropy (CE) criterion, and least mean square (LMS) criterion (all for multi-class classification problems) are supported in it. The program uses a sparse-data structure to represent the feature vector to seek higher computational speed. Some other techniques such as online updating, weights averaging, gaussian prior regularization are also supported. Changes:Initial Announcement on mloss.org.
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About: This is demo program on global thresholding for image of bright small objects, such as aircrafts in airports. the program include four method, otsu,2D-Tsallis,PSSIM, Smoothnees Method. Changes:Initial Announcement on mloss.org.
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About: Operator Discretization Library (ODL) is a Python library that enables research in inverse problems on realistic or real data. Changes:Initial Announcement on mloss.org.
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About: This algorithm is described in Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval. See https://github.com/zhaofang0627/cuda-convnet-for-hashing Changes:Initial Announcement on mloss.org.
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About: The proposed hashing algorithm leverages the bootstrap sampling idea and integrates it with PCA, resulting in a new projection method called Bagging PCA Hashing. Changes:Initial Announcement on mloss.org.
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About: Scilab Pattern Recognition Toolbox is a toolbox developed for Scilab software, and is used in pattern recognition, machine learning and the related field. It is developed for the purpose of education and research. Changes:Initial Announcement on mloss.org.
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About: Supervised Latent Semantic Indexing(SLSI) is an supervised feature transformation method. The algorithms in this package are based on the iterative algorithm of Latent Semantic Indexing. Changes:Initial Announcement on mloss.org.
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About: LDPar is an efficient data-driven dependency parser. You can train your own parsing model on treebank data and parse new data using the induced model. Changes:Initial Announcement on mloss.org.
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About: Q. Dong, Two-dimensional relaxed representation, Neurocomputing, 121:248-253, 2013, http://dx.doi.org/10.1016/j.neucom.2013.04.044 Changes:Initial Announcement on mloss.org.
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About: Jie Gui, Zhenan Sun, Guangqi Hou, Tieniu Tan, "An optimal set of code words and correntropy for rotated least squares regression", International Joint Conference on Biometrics, 2014, pp. 1-6 Changes:Initial Announcement on mloss.org.
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About: Armadillo/C++ implementation of the Indefinite Core Vector Machine Changes:Some tiny errors in the Nystroem demo scripts - should be ok now Initial Announcement on mloss.org.
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About: Jie Gui et al., "How to estimate the regularization parameter for spectral regression discriminant analysis and its kernel version?", IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 2, pp. 211-223, 2014 Changes:Initial Announcement on mloss.org. |
About: Boosting Methods for GAMLSS Models Changes:Fetched by r-cran-robot on 2013-04-01 00:00:04.956804
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About: Obandit is an Ocaml module for multi-armed bandits. It supports the EXP, UCB and Epsilon-greedy family of algorithms. Changes:Initial Announcement on mloss.org.
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About: Stepwise Diagonal Discriminant Analysis Changes:Fetched by r-cran-robot on 2012-02-01 00:00:11.677447
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About: A non-iterative, incremental and hyperparameter-free learning method for one-layer feedforward neural networks without hidden layers. This method efficiently obtains the optimal parameters of the network, regardless of whether the data contains a greater number of samples than variables or vice versa. It does this by using a square loss function that measures errors before the output activation functions and scales them by the slope of these functions at each data point. The outcome is a system of linear equations that obtain the network's weights and that is further transformed using Singular Value Decomposition. Changes:Initial Announcement on mloss.org.
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About: ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models. ruptures focuses on ease of use by providing a well-documented and consistent interface. In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package. Changes:Initial Announcement on mloss.org.
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