20 projects found that use c++ as the programming language.
Showing Items 41-60 of 193 on page 3 of 10: Previous 1 2 3 4 5 6 7 8 Next Last

Logo RLPy 1.3a

by bobklein2 - August 28, 2014, 14:34:35 CET [ Project Homepage BibTeX Download ] 5207 views, 1126 downloads, 1 subscription

About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting).

Changes:
  • Fixed bug where results using same random seed were different with visualization turned on/off
  • Created RLPy package on pypi (Available at https://pypi.python.org/pypi/rlpy)
  • Switched from custom logger class to python default
  • Added unit tests
  • Code readability improvements (formatting, variable names/ordering)
  • Restructured TD Learning heirarchy
  • Updated tutorials

Logo CURFIL 1.1

by hanschul - August 18, 2014, 13:54:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2431 views, 578 downloads, 1 subscription

About: CURFIL uses NVIDIA CUDA to accelerate random forest training and prediction for RGB and RGB-D images. It focuses on image labelling tasks, such as image segmentation or classification applications. CURFIL allows to search for optimal hyper-parameter configurations (e.g. using the hyperopt) package) by massively decreasing training time.

Changes:

Initial Announcement on mloss.org.


Logo Caffe 0.9999

by sergeyk - August 9, 2014, 01:57:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11080 views, 1793 downloads, 2 subscriptions

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

Changes:

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999


Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 6393 views, 1856 downloads, 3 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 JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 37999 views, 9865 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


Logo ARTOS Adaptive Realtime Object Detection System 1.0

by erik - July 11, 2014, 22:02:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3103 views, 648 downloads, 2 subscriptions

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.


Logo Semi Stochastic Gradient Descent 1.0

by konkey - July 9, 2014, 04:28:47 CET [ BibTeX BibTeX for corresponding Paper Download ] 2932 views, 813 downloads, 1 subscription

About: Efficient implementation of Semi-Stochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2-regularized).

Changes:

Initial Announcement on mloss.org.


Logo Encog Machine Learning Framework 3.2

by jeffheaton - July 5, 2014, 23:47:06 CET [ Project Homepage BibTeX Download ] 6447 views, 2259 downloads, 1 subscription

About: Encog is a Machine Learning framework for Java, C#, Javascript and C/C++ that supports SVM's, Genetic Programming, Bayesian Networks, Hidden Markov Models and other algorithms.

Changes:

Changes for Encog 3.2:

Issue #53: Fix Out Of Range Bug In BasicMLSequenceSet. Issue #52: Unhandled exception in Encog.Util.File.ResourceLoader.CreateStream (ResourceLoader.cs) Issue #50: Concurrency bugs in PruneIncremental Issue #48: Unit Tests Failing - TestHessian Issue #46: Couple of small fixes - Temporal DataSet and SCG training Issue #45: Fixed EndMinutesStrategy to correctly evaluate ShouldStop after the specified number of minutes have elapsed. Issue #44: Encog.ML.Data.Basic.BasicMLDataPairCentroid.Add() & .Remove() Issue #43: Unit Tests Failing - Matrix not full rank Issue #42: Nuget - NuSpec Issue #36: Load Examples easier


Logo RFD 1.0

by openpr_nlpr - April 28, 2014, 10:34:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2778 views, 569 downloads, 1 subscription

About: This is an unoptimized implementation of the RFD binary descriptor, which is published in the following paper. B. Fan, et al. Receptive Fields Selection for Binary Feature Description. IEEE Transaction on Image Processing, 2014. doi: http://dx.doi.org/10.1109/TIP.2014.2317981

Changes:

Initial Announcement on mloss.org.


About: RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporal-difference learning algorithms in Reinforcement Learning. It is an optimized library for robotic applications and embedded devices that operates under fast duty cycles (e.g., < 30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, physical NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behaviors, and represent learnable knowledge. The implementation of the RLLib library is inspired by the RLPark API, which is a library of temporal-difference learning algorithms written in Java.

Changes:

Current release version is v2.0.


Logo MShadow 1.0

by antinucleon - April 10, 2014, 02:57:54 CET [ Project Homepage BibTeX Download ] 2422 views, 695 downloads, 1 subscription

About: Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. Support element-wise expression expand in high performance. Code once, run smoothly on both GPU and CPU

Changes:

Initial Announcement on mloss.org.


Logo CXXNET 0.1

by antinucleon - April 10, 2014, 02:47:08 CET [ Project Homepage BibTeX Download ] 3098 views, 727 downloads, 1 subscription

About: CXXNET (spelled as: C plus plus net) is a neural network toolkit build on mshadow(https://github.com/tqchen/mshadow). It is yet another implementation of (convolutional) neural network. It is in C++, with about 1000 lines of network layer implementations, easily configuration via config file, and can get the state of art performance.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Tapkee 1.0

by blackburn - April 10, 2014, 02:45:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12226 views, 3506 downloads, 1 subscription

About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction.

Changes:

Initial Announcement on mloss.org.


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 38859 views, 6396 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


Logo JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12673 views, 4236 downloads, 2 subscriptions

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

Changes:

The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:

  1. Support for multithreading in training and prediction with ensemble models. Since both of these are embarassingly parallel, this has induced a significant speedup (3-fold on quad-core).
  2. Extensive programming framework for aggregation of base model predictions which allows highly efficient prototyping of new aggregation approaches. Additionally we provide several predefined strategies, including (weighted) majority voting, logistic regression and nonlinear SVMs of your choice -- be sure to check out the esvm-edit tool! The provided framework also allows you to efficiently program your own, novel aggregation schemes.
  3. Full code transition to C++11, the latest C++ standard, which enabled various performance improvements. The new release requires moderately recent compilers, such as gcc 4.7.2+ or clang 3.2+.
  4. Generic implementations of convenient facilities have been added, such as thread pools, deserialization factories and more.

The API and ABI have undergone significant changes, many of which are due to the transition to C++11.


Logo HierLearning 1.0

by neville - March 2, 2014, 04:24:37 CET [ BibTeX BibTeX for corresponding Paper Download ] 2483 views, 687 downloads, 1 subscription

About: HierLearning is a C++11 implementation of a general-purpose, multi-agent, hierarchical reinforcement learning system for sequential decision problems.

Changes:

Initial Announcement on mloss.org.


Logo JMLR BudgetedSVM v1.1

by nemanja - February 12, 2014, 20:53:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4025 views, 775 downloads, 1 subscription

About: BudgetedSVM is an open-source C++ toolbox for scalable non-linear classification. The toolbox can be seen as a missing link between LibLinear and LibSVM, combining the efficiency of linear with the accuracy of kernel SVM. We provide an Application Programming Interface for efficient training and testing of non-linear classifiers, supported by data structures designed for handling data which cannot fit in memory. We also provide command-line and Matlab interfaces, providing users with an efficient, easy-to-use tool for large-scale non-linear classification.

Changes:

Changed license from LGPL v3 to Modified BSD.


Logo A Parallel LDA Learning Toolbox 1.0

by yanjianfeng - January 24, 2014, 11:48:07 CET [ BibTeX Download ] 2865 views, 1074 downloads, 1 subscription

About: We introduces PLL, a parallel LDA learning toolbox for big topic modeling.

Changes:

Fix some compiling errors.


Logo DRVQ 1.0.1-beta

by iavr - January 18, 2014, 17:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2820 views, 632 downloads, 1 subscription

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.


Logo Gesture Recogition Toolkit 0.1 Revision 289

by ngillian - December 13, 2013, 22:59:53 CET [ Project Homepage BibTeX Download ] 7621 views, 1435 downloads, 1 subscription

About: The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library that has been specifically designed for real-time gesture recognition. It features a large number of machine-learning algorithms for both classification and regression in addition to a wide range of supporting algorithms for pre-processing, feature extraction and dataset management. The GRT has been designed for real-time gesture recognition, but it can also be applied to more general machine-learning tasks.

Changes:

Added Decision Tree and Random Forests.


Showing Items 41-60 of 193 on page 3 of 10: Previous 1 2 3 4 5 6 7 8 Next Last