Search Results for "q learning algorithm" - Page 9

Showing 277 open source projects for "q learning algorithm"

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  • 1

    STELLA Data Reduction Pipeline

    The STELLA Data-Reduction Pipeline for all kinds of Spectra

    ...If you use any of these programs to reduce data for a publication you must cite the paper "A Fast and Portable Reimplementation of Piskunov and Valenti's Optimal Extraction Algorithm with improved Cosmic Ray Removal and Optimal Sky Subtraction" by A. Ritter, E. A. Hyde, and Q. A. Parker, published in PASP 126, February 2014.
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  • 2

    Face Recognition System

    Face Recognition System Matlab source code

    Research on automatic face recognition in images has rapidly developed into several inter-related lines, and this research has both lead to and been driven by a disparate and expanding set of commercial applications. The large number of research activities is evident in the growing number of scientific communications published on subjects related to face processing and recognition. Index Terms: face, recognition, eigenfaces, eigenvalues, eigenvectors, Karhunen-Loeve algorithm.
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  • 3
    MODLEM

    MODLEM

    rule-based, WEKA compatible, Machine Learning algorithm

    This project is a WEKA (Waikato Environment for Knowledge Analysis) compatible implementation of MODLEM - a Machine Learning algorithm which induces minimum set of rules. These rules can be adopted as a classifier (in terms of ML). It is a sequential covering algorithm, which was invented to cope with numeric data without discretization. Actually the nominal and numeric attributes are treated in the same way: attribute's space is being searched to find the best rule condition during rule induction. ...
    Downloads: 7 This Week
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  • 4

    Fingerprint Recognition System

    Fingerprint Recognition System 5.3 - Matlab source code

    The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a...
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  • 5
    Intelligent Keyword Miner

    Intelligent Keyword Miner

    Intelligent SEO keyword miner and predicing tool

    THIS IS A NETBEANS 8.02 PROJECT ENGLISH ONLY This program was made to help me with the patent research. It simply generates the search keywords, based on your upvotes or a downvotes of the input parameters. It can accept a text or URL (text takes a prescedence over the URL). If you input URL, it goes to a page, and learns its text from HTML format. This program is intelligent as it predicts what you may want to search next, based on your personal trends. After searching the...
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  • 6
    ArabicDiacritizer

    ArabicDiacritizer

    An automatic restoration of Arabic diacritic marks

    This is a software of Arabic diacritical marks restoration. It is based mainly on deep architectures using deep neural network. The algorithm generates diacritized text with determined end case. The algorithm is described in detail in: Ilyes Rebai, and Yassine BenAyed 'Text-to-speech synthesis system with Arabic diacritic recognition system', Computer Speech & Language, 2015. We appreciate it very much if you can cite our related work. ************** Installation...
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  • 7

    LWPR

    Locally Weighted Projection Regression (LWPR)

    ...Please cite: [1] Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005). [2] Stefan Klanke, Sethu Vijayakumar and Stefan Schaal, A Library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623--626 (2008). More details and usage guidelines on the code website.
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  • 8
    Decision Tree

    Decision Tree

    ID3-based implementation of the ML Decision Tree algorithm

    DecisionTree is a Ruby library that implements decision tree learning with the ID3 information-gain algorithm. It can train models from discrete, continuous, or mixed attribute data. Continuous features are evaluated across possible split points to build threshold-based binary branches. Discrete models classify unique labels and can be rendered for visual inspection. The library supports inconsistent datasets, multiple or symbolic outputs, and fallback values when no branch matches an input. ...
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  • 9
    Fuzzy clustering variation looks for a good subset of attributes in order to improve the classification accuracy of supervised learning techniques in classification problems with a huge number of attributes involved. It first creates a ranking of attributes based on the Variation value, then divide into two groups, last using Verification method to select the best group.Simon Fong, Justin Liang, YanZhuang, "Improving Classification Accuracy Using Fuzzy Clustering Coefficients of Variations (FCCV) Feature Selection Algorithm", 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), pp.147-151
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  • 10
    ConvNetJS

    ConvNetJS

    Deep learning in Javascript to train convolutional neural networks

    ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. ...
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  • 11

    A2y

    Automated Algorithm Synthesis

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  • 12
    Machine learning library that performs several clustering algorithms (k-means, incremental k-means, DBSCAN, incremental DBSCAN, mitosis, incremental mitosis, mean shift and SHC) and performs several semi-supervised machine learning approaches (self-learning and co-training). --------------------------------------------------------------------------- To run the library, just double click on the jar file. Also, you can use the following command line: Java -Xms1500m -jar "ML Library.jar"...
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  • 13

    EducationalLCS

    eLCS - Educational Learning Classifier System

    Educational Learning Classifier System (eLCS) is a set of learning classifier system (LCS) educational demos designed to introduce students or researchers to the basics of a modern Michigan-style LCS algorithm. This eLCS package includes 5 different implementations of a basic LCS algorithm, as part of a 6 stage set of demos that will be paired with the first introductory LCS textbook.
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  • 14

    sdEM

    Stochastic Discriminative Expectation Maximization (sdEM)

    Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for discriminative training of probabilistic generative models belonging to the natural exponential family. In this work, we introduce and justify this algorithm as a stochastic natural gradient descent method, i.e. a method which accounts for the information geometry in the parameter space of the statistical model. We show how this learning algorithm can be used to train probabilistic generative models by minimizing different discriminative loss functions, such as the negative conditional log-likelihood and the Hinge loss. ...
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  • 15
    ...Updates: FrameBot now uses a kmer pre-filtering heuristic for no-metric-search. This pre-filtering may increase the speed by one to two orders of magnitude. CITATION: Wang, Q., Quensen, J. F., Fish, J. A., Lee, T. K., Sun, Y., Tiedje, J. M., and Cole, J. R. 2013. Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool. mBio 4:e00592-13.
    Downloads: 2 This Week
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  • 16

    CRN for learning

    Work done on CRN in 2010

    code for the paper http://www.google.com.pk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&ved=0CCYQFjAB&url=http%3A%2F%2Fwww.thinkmind.org%2Fdownload.php%3Farticleid%3Dcocora_2012_2_10_60013&ei=BMGZUPLJIM3LqAHjx4D4BQ&usg=AFQjCNHHBhK8SjSEapEfxw5Lq-ydYyAtiA
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  • 17

    PyVision Computer Vision Toolkit

    A Python computer vision library

    PyVision is a object-oriented Computer Vision Toolkit for researchers that contains vision and machine learning algorithms and algorithm analysis and easily interfaces with scipy/numpy, PIL, opencv and other computer and machine learning libraries.
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  • 18

    iOVFDT

    iOVFDT algorithm of incremental decision tree

    ...To solve this trade-off, we propose a new decision tree so called incrementally optimized very fast decision tree (iOVFDT). Inheriting the use of Hoeffding bound in VFDT algorithm for node-splitting check, it contains four optional strategies of functional tree leaf, which improve the classifying accuracy. In addition, a multi-objective incremental optimization mechanism investigates a balance among accuracy, mode size and learning speed...
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  • 19

    StabLe

    An algorithm for learning stable graphical models from data

    Stable Graphical Model Learning (StabLe) is an algorithm for learning the structure and parameters of stable graphical (SG) models from data. Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon.
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  • 20

    ProximityForest

    Efficient Approximate Nearest Neighbors for General Metric Spaces

    A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer...
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  • 21
    MIT SPHERES Simulation (Release)

    MIT SPHERES Simulation (Release)

    MIT's spacecraft simulator for control algorithm development

    ...The simulation code base consists of simulated versions of most of the SPHERES core flight code and additional code that simulates dynamics, communications, and other environmental interaction. The simulation is particularly valuable during the early stages of algorithm development and implementation as an aid in accelerating the learning curve for any Guest Scientist for SPHERES. Algorithms may be implemented in C or Embedded MATLAB and executed in the MATLAB simulation environment to verify general desired behavior. With some limitations, the code used in the simulation can be directly transferred to the SPHERES hardware. ...
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  • 22
    NNBrain

    NNBrain

    A free, open source collection of neural network based AI agents.

    NNBrain is a free and open source collection of artificial intelligence agents. These agents have applications in video games, research, business, and more. The included agents function in both discrete and continuous action and state spaces.
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  • 23
    HAWK - PDF Text Search Java Project

    HAWK - PDF Text Search Java Project

    No more support for this project - TAKE A LOOK AT FALCONSEARCH

    No more support for this project - TAKE A LOOK AT FALCONSEARCH "https://sourceforge.net/projects/falcontextsearch/"
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  • 24
    Java application for training and deploying text processing applications such as part-of-speech taggers, based on a re-implementation of Brill's algorithm in Java.
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  • 25

    LTS: Learning to Search

    Discriminative subgraph mining by learning from search history

    LTS (Learning to Search) is an implementation of an algorithm described in "LTS: Discriminative Subgraph Mining by Learning from Search History" in Data Engineering (ICDE), IEEE 27th International Conference, pages 207-218, 2011. The purpose of LTS is to find discriminative subgraphs, which are smaller graphs that are embedded in larger graphs that all share a certain trait.
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