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Showing Items 101-110 of 628 on page 11 of 63: First Previous 6 7 8 9 10 11 12 13 14 15 16 Next Last

Logo ABACOC Adaptive Ball Cover for Classification 2.0

by kikot - May 29, 2015, 11:57:28 CET [ BibTeX BibTeX for corresponding Paper Download ] 5213 views, 1302 downloads, 3 subscriptions

About: Incremental (Online) Nonparametric Classifier. You can classify both points (standard) or matrices (multivariate time series). Java and Matlab code already available.

Changes:

version 2: parameterless system, constant model size, prediction confidence (for active learning).

NEW!! C++ version at: https://github.com/ilaria-gori/ABACOC


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: 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.


Logo ClusterEval 1.1

by cdevries - May 18, 2015, 22:01:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5717 views, 1329 downloads, 2 subscriptions

About: Cluster quality Evaluation software. Implements cluster quality metrics based on ground truths such as Purity, Entropy, Negentropy, F1 and NMI. It includes a novel approach to correct for pathological or ineffective clusterings called 'Divergence from a Random Baseline'.

Changes:

Moved project to GitHub.


Logo GESL v1.01

by bellet - May 15, 2015, 11:54:04 CET [ BibTeX BibTeX for corresponding Paper Download ] 3644 views, 1382 downloads, 1 subscription

About: Learning string edit distance / similarity from data

Changes:

Added datasets used in the experiments of the paper


Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 15570 views, 2754 downloads, 3 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

Changes:
  • Distributed version of xgboost that runs on YARN, scales to billions of examples

  • Direct save/load data and model from/to S3 and HDFS

  • Feature importance visualization in R module, by Michael Benesty

  • Predict leaf index

  • Poisson regression for counts data

  • Early stopping option in training

  • Native save load support in R and python

  • xgboost models now can be saved using save/load in R

  • xgboost python model is now pickable

  • sklearn wrapper is supported in python module

  • Experimental External memory version


Logo lomo feature extraction and xqda metric learning for person reidentification 1.0

by openpr_nlpr - May 6, 2015, 11:38:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2792 views, 418 downloads, 3 subscriptions

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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.


Logo Nilearn 0.1.2

by goulagman - April 29, 2015, 16:16:25 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3121 views, 740 downloads, 3 subscriptions

About: Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Changes:

Initial Announcement on mloss.org.


About: FAST is an implementation of Hidden Markov Models with Features. It allows features to modify both emissions and transition probabilities.

Changes:

Initial Announcement on mloss.org.


Logo MIPS, The migrant implementation system 1.0

by thomasfannes - April 28, 2015, 15:07:05 CET [ Project Homepage BibTeX Download ] 2103 views, 590 downloads, 3 subscriptions

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


Showing Items 101-110 of 628 on page 11 of 63: First Previous 6 7 8 9 10 11 12 13 14 15 16 Next Last