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About: This database include 164 satellite iamges of different airports from google-earth, the first 110 images are used as training images, include 2337 aircrafts, the remained 54 images are used as test images, include 2206 aircrafts, each aircraft are labeled by two points and one number, indicating the positions of head and tail,and which point is the head. The labeled informations are recorded in two files: train.txt and test.txt, matlab is recommanded to be used for reading these data by import data tool. Changes:Initial Announcement on mloss.org.
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About: The scancity of open database of small targets in remotesensing field has hindered the research in a lot. This open database includes 111 satellite images from google earth, 80 images are used as training set, contains 7862 vehicles. The other 31 images sre used as test set, contains 1635 vehicles. The positions of each vehicle are labeled by two points, which are recorded in "train.txt" and "test.txt" respectively. The label information is easily to be read by matlab tool. So such an open database is very useful for researchers in object detection fields. Changes:Initial Announcement on mloss.org.
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About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models. Changes:Logdet-estimation functionality for grid-based approximate covariances
More generic infEP functionality
New infKL function contributed by Emtiyaz Khan and Wu Lin
Time-series covariance functions on the positive real line
New covariance functions
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About: A Matlab benchmarking toolbox for online and adaptive regression with kernels. Changes:
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About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, unsupervised feature learning and clustering tasks. Changes:
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About: This MATLAB package provides the MLAPG algorithm proposed in our ICCV 2015 paper. It is efficient for PSD constrained metric learning, and also effective for person re-identification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/mlapg/. 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: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields. Changes:Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.
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About: It is a Scala library for building Bayesian Networks with discrete/continuous variables and running deterministic Bayesian inference 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: GradMC is an algorithm for MR motion artifact removal implemented in Matlab Changes:Added support for multi-rigid motion correction.
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About: minFunc is a Matlab function for unconstrained optimization of differentiable real-valued multivariate functions using line-search methods. It uses an interface very similar to the Matlab Optimization Toolbox function fminunc, and can be called as a replacement for this function. On many problems, minFunc requires fewer function evaluations to converge than fminunc (or minimize.m). Further it can optimize problems with a much larger number of variables (fminunc is restricted to several thousand variables), and uses a line search that is robust to several common function pathologies. Changes:Initial Announcement on mloss.org.
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About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference. Changes:added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used
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About: Data-efficient policy search framework using probabilistic Gaussian process models Changes:Initial Announcement on mloss.org.
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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: Gaussian process RTS smoothing (forward-backward smoothing) based on moment matching. Changes:Initial Announcement on mloss.org.
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About: The software provides an implementation of a filter/smoother based on Gibbs sampling, which can be used for inference in dynamical systems. Changes:Initial Announcement on mloss.org.
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About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files. Changes:Initial Announcement on mloss.org
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About: Orthonormal wavelet transform for D dimensional tensors in L levels. Generic quadrature mirror filters and tensor sizes. Runtime is O(n), plain C, MEX-wrapper and demo provided. Changes:Initial Announcement on mloss.org. |
