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Showing Items 221-240 of 674 on page 12 of 34: First Previous 7 8 9 10 11 12 13 14 15 16 17 Next Last

Logo MICP 1.04

by kay_brodersen - March 26, 2013, 12:42:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12328 views, 2462 downloads, 0 subscriptions

About: This toolbox implements models for Bayesian mixed-effects inference on classification performance in hierarchical classification analyses.

Changes:

In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.


Logo TBEEF, Triple Bagged Ensemble Ensemble Framework 3.2

by ChrisRackauckas - April 16, 2016, 03:51:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12305 views, 2432 downloads, 0 subscriptions

About: TBEEF, a doubly ensemble framework for recommendation and prediction problems.

Changes:

Included the final technical report.


Logo GraphLab v1-1908

by dannybickson - November 22, 2011, 12:50:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12283 views, 2126 downloads, 0 subscriptions

About: Multicore/distributed large scale machine learning framework.

Changes:

Update version.


Logo LSTM for biological sequence analysis 1.0

by mhex - July 28, 2010, 16:32:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12237 views, 2832 downloads, 0 subscriptions

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About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included.

Changes:

Spectrum LSTM package included


Logo Aika 0.17

by molzberger - May 14, 2018, 15:42:00 CET [ Project Homepage BibTeX Download ] 12202 views, 3299 downloads, 0 subscriptions

About: Aika is an open source text mining engine. It can automatically extract and annotate semantic information in text. In case this information is ambiguous, Aika will generate several hypothetical interpretations about the meaning of this text and retrieve the most likely one.

Changes:

Aika Version 0.17 2018-05-14

  • Introduction of synapse relations. Previously the relation between synapses was implicitly modeled through word positions (RIDs). Now it is possible to explicitly model relations like: The end position of input activation 1 equals to the begin position of input activation 2. Two types of relations are currently supported, range relations and instance relations. Range relations compare the input activation range of a given synapse with that of the linked synapse. Instance relations also compare the input activations of two synapses, but instead of the ranges the dependency relations of these activations are compared.
  • Removed the norm term from the interpretation objective function.
  • Introduction of an optional distance function to synapses. It allows to model a weakening signal depending on the distance of the activation ranges.
  • Example implementation of a context free grammar.
  • Example implementation for co-reference resolution.
  • Work on an syllable identification experiment based on the meta network implementation.

Aika Version 0.15 2018-03-16

  • Simplified interpretation handling by removing the InterpretationNode class and moving the remaining logic to the Activation class.
  • Moved the activation linking and activation selection code to separate classes.
  • Ongoing work on the training algorithms.

Logo cbMDS Correlation Based Multi Dimensional Scaling 1.2

by emstrick - July 27, 2013, 14:35:36 CET [ BibTeX BibTeX for corresponding Paper Download ] 12173 views, 2721 downloads, 0 subscriptions

About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

Changes:
  • Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

  • Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

  • Ver 1.2 Added reconstruction of sparse relationship matrices, Fri Jul 26 16:58:37 CEST 2013


Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 12135 views, 3387 downloads, 0 subscriptions

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

Changes:
  • added ElasticNets as a regularization option
  • fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
  • added a couple of I/O optimizations

Logo mcmkl 0.1

by ong - May 15, 2008, 15:30:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12100 views, 2454 downloads, 0 subscriptions

About: We provide some preliminary code for multiclass multiple kernel learning in Matlab using CPLEX as a base solver.

Changes:

Initial Announcement on mloss.org.


Logo BSVM 2.06

by biconnect - January 30, 2008, 10:27:13 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12077 views, 2315 downloads, 0 subscriptions

About: BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes three methods

Changes:

Initial Announcement on mloss.org.


Logo Disco 0.1

by tuulos - October 6, 2008, 11:14:48 CET [ Project Homepage BibTeX Download ] 12020 views, 2212 downloads, 0 subscriptions

About: Disco is an open-source implementation of the [Map-Reduce framework](http://en.wikipedia.org/wiki/MapReduce) for distributed computing. As the original framework, Disco supports parallel [...]

Changes:

Initial Announcement on mloss.org.


Logo High Dimensional Data Clustering with Gaussian Mixtures 1.1

by cbouveyron - January 14, 2008, 08:53:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12014 views, 2068 downloads, 0 subscriptions

About: The High-Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifier for high-dimensional data. This classifier is based on a mixture of Gaussian models adapted for [...]

Changes:

Initial Announcement on mloss.org.


Logo TMBP 1.0

by zengjia - April 5, 2012, 06:42:26 CET [ BibTeX BibTeX for corresponding Paper Download ] 11961 views, 6059 downloads, 0 subscriptions

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About: Message passing for topic modeling

Changes:
  1. improve "readme.pdf".
  2. correct some compilation errors.

Logo Toeblitz Toolkit for Fast Toeplitz Matrix Operations 1.03

by cunningham - August 13, 2014, 02:21:36 CET [ BibTeX Download ] 11820 views, 3134 downloads, 0 subscriptions

About: Toeblitz is a MATLAB/Octave package for operations on positive definite Toeplitz matrices. It can solve Toeplitz systems Tx = b in O(n*log(n)) time and O(n) memory, compute matrix inverses T^(-1) (with free log determinant) in O(n^2) time and memory, compute log determinants (without inverses) in O(n^2) time and O(n) memory, and compute traces of products A*T for any matrix A, in minimal O(n^2) time and memory.

Changes:

Adding a write-up in written/toeblitz.pdf describing the package.


Logo PyML a python machine learning library focused on kernel methods 0.7.0

by asa - May 29, 2008, 22:23:39 CET [ Project Homepage BibTeX Download ] 11802 views, 3067 downloads, 0 comments, 0 subscriptions

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About: PyML is an interactive object oriented framework for machine learning in python with a focus on kernel methods.

Changes:

Initial Announcement on mloss.org.


Logo MOSIS 0.55

by claasahl - March 9, 2014, 17:35:40 CET [ BibTeX Download ] 11795 views, 3560 downloads, 0 subscriptions

About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications.

Changes:
  • Move "flow"-related classes into package "de.claas.mosis.flow" (e.g. Node and Link).
  • Refined and improved "flow"-related tests (e.g. Iterator and Node tests).
  • Refactored tests for data formats (e.g. PlainText and JSON tests).
  • Added visitor design pattern for graph-based functions (e.g. initialization and processing).
  • Documented parameters of Processor implementations.

Logo HDDM 0.5

by Wiecki - April 24, 2013, 02:53:07 CET [ Project Homepage BibTeX Download ] 11623 views, 2647 downloads, 0 subscriptions

About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.

Changes:
  • New and improved HDDM model with the following changes:
    • Priors: by default model will use informative priors (see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm) If you want uninformative priors, set informative=False.
    • Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
    • Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
    • The old model has been renamed to HDDMTransformed.
    • HDDMRegression and HDDMStimCoding are also using this model.
  • HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
  • Improved online documentation at http://ski.clps.brown.edu/hddm_docs
  • A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
  • Ratcliff's quantile optimization method for single subjects and groups using the .optimize() method
  • Maximum likelihood optimization.
  • Many bugfixes and better test coverage.
  • hddm_fit.py command line utility is depracated.

Logo pySPACE 1.2

by krell84 - October 29, 2014, 15:36:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11551 views, 2171 downloads, 0 subscriptions

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.

Changes:

improved testing, improved documentation, windows compatibility, more algorithms


Logo Rchemcpp 1.99.0

by klambaue - September 10, 2013, 09:10:13 CET [ Project Homepage BibTeX Download ] 11502 views, 2544 downloads, 0 subscriptions

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About: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules.

Changes:

Moved from CRAN to Bioconductor. Improved handling of molecules, visualization and examples.


Logo Chalearn gesture challenge code by jun wan 2.0

by joewan - September 29, 2015, 08:50:22 CET [ BibTeX BibTeX for corresponding Paper Download ] 11458 views, 2565 downloads, 0 subscriptions

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-grplasso 0.4-2

by r-cran-robot - April 1, 2009, 00:00:00 CET [ Project Homepage BibTeX Download ] 11424 views, 2398 downloads, 0 subscriptions

About: Fitting user specified models with Group Lasso penalty

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.428021


Showing Items 221-240 of 674 on page 12 of 34: First Previous 7 8 9 10 11 12 13 14 15 16 17 Next Last