https://www.bibsonomy.org/user/gromgull/machine-learningBibSonomy RSS Feed for /user/gromgull/machine-learningThis tutorial will provide an introduction to tensor factorizations and their applications for machine learning on graphs.http://www.cip.ifi.lmu.de/~nickel/iswc2012-learning-on-linked-data-with-tensors/gromgull2012-12-03T11:32:01+01:00semantic-web machine-learning linked-open-data <span itemprop="description">This tutorial will provide an introduction to tensor factorizations and their applications for machine learning on graphs.</span>Homepage for Algorithms for Massive Data Sets at DTU Informaticshttp://massivedatasets.wordpress.com/gromgull2012-11-28T16:21:47+01:00teaching courseware courese machine-learning <span itemprop="description">Homepage for Algorithms for Massive Data Sets at DTU Informatics</span>FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.http://factorie.cs.umass.edu/gromgull2012-10-31T10:45:00+01:00mccallum scala machine-learning graphical-models <span itemprop="description">FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.</span>Pattern is a web mining module for the Python programming language. It bundles tools for data retrieval (Google + Twitter + Wikipedia API, web spider, HTML DOM parser), text analysis (rule-based shallow parser, WordNet interface, syntactical + semantical n-gram search algorithm, tf-idf + cosine similarity + LSA metrics), clustering and classification (k-means, KNN, SVM), and data visualization (graph networks).http://www.clips.ua.ac.be/pages/patterngromgull2012-07-15T09:30:58+02:00machine-learning web-mining nlp python library <span itemprop="description">Pattern is a web mining module for the Python programming language. It bundles tools for data retrieval (Google + Twitter + Wikipedia API, web spider, HTML DOM parser), text analysis (rule-based shallow parser, WordNet interface, syntactical + semantical n-gram search algorithm, tf-idf + cosine similarity + LSA metrics), clustering and classification (k-means, KNN, SVM), and data visualization (graph networks).</span>http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/gromgull2012-05-29T12:08:16+02:00big-data machine-learning approximation heuristics data-structures online-learning <a itemprop="url" data-versiondate="2012-05-29T12:08:16+02:00" href="http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/" rel="nofollow" class="description-link">http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/</a>Another cloud computing service for prediction. This time based on a massive joint-prob model across all variables.http://priorknowledge.com/gromgull2012-05-04T08:22:18+02:00machine-learning web-service cloud-computing <span itemprop="description">Another cloud computing service for prediction. This time based on a massive joint-prob model across all variables.</span>BigML is a web-service for running distributed ML. 64gb free upload, API in python. http://blog.bigml.com/2012/05/04/machine-learning-in-python-has-never-been-easier/gromgull2012-05-04T08:06:36+02:00machine-learning python api cloud-computing <span itemprop="description">BigML is a web-service for running distributed ML. 64gb free upload, API in python. </span>HEigen is a spectral analysis tool which computes top k eigenvalues and corresponding eigenvectors of extremely large(~billions of nodes and edges) graphs. HEigen runs on top of Hadoop platform. http://www.cs.cmu.edu/~ukang/HEIGEN/gromgull2012-02-22T17:20:55+01:00graph-processing machine-learning eigen hadoop <span itemprop="description">HEigen is a spectral analysis tool which computes top k eigenvalues and corresponding eigenvectors of extremely large(~billions of nodes and edges) graphs. HEigen runs on top of Hadoop platform. </span>The Apache Giraph project is a fault-tolerant in-memory distributed graph processing system which runs on top of a standard Hadoop installation.http://ssc.io/why-apache-giraph-is-more-than-a-graph-processing-system/gromgull2012-02-22T17:11:51+01:00machine-learning map-reduce cluster-computing <span itemprop="description">The Apache Giraph project is a fault-tolerant in-memory distributed graph processing system which runs on top of a standard Hadoop installation.</span>GraphLab: A Parallel Framework for Machine Learninghttp://graphlab.org/gromgull2012-02-22T17:10:08+01:00parallel-computing GraphLab machine-learning graph graph-processing <span itemprop="description">GraphLab: A Parallel Framework for Machine Learning</span>http://www.azintablog.com/2010/10/16/gpu-large-scale-data-mining/gromgull2012-02-22T17:06:14+01:00machine-learning gpu cuda <a itemprop="url" data-versiondate="2012-02-22T17:06:14+01:00" href="http://www.azintablog.com/2010/10/16/gpu-large-scale-data-mining/" rel="nofollow" class="description-link">http://www.azintablog.com/2010/10/16/gpu-large-scale-data-mining/</a>Pegasus An award-winning, open-source, graph-mining system with massive scalability. Analyze petabytes of graph data with ease. http://www.cs.cmu.edu/~pegasus/gromgull2012-02-13T11:17:10+01:00machine-learning graph-mining <span itemprop="description">Pegasus An award-winning, open-source, graph-mining system with massive scalability. Analyze petabytes of graph data with ease. </span>DMA 2012 : Workshop on Data Mining in Agriculturehttp://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=19728gromgull2011-11-21T14:28:55+01:00workshop data-mining machine-learning igreen agriculture <span itemprop="description">DMA 2012 : Workshop on Data Mining in Agriculture</span>SUBDUE is a graph-based knowledge discovery system that finds structural, relational patterns in data representing entities and relationships. SUBDUE represents data using a labeled, directed graph in which entities are represented by labeled vertices or subgraphs, and relationships are represented by labeled edges between the entities. SUBDUE uses the minimum description length (MDL) principle to identify patterns that minimize the number of bits needed to describe the input graph after being compressed by the pattern. SUBDUE can perform several learning tasks, including unsupervised learning, supervised learning, clustering and graph grammar learning. http://ailab.wsu.edu/subdue/gromgull2011-10-17T13:56:41+02:00machine-learning data-mining graphs <span itemprop="description">SUBDUE is a graph-based knowledge discovery system that finds structural, relational patterns in data representing entities and relationships. SUBDUE represents data using a labeled, directed graph in which entities are represented by labeled vertices or subgraphs, and relationships are represented by labeled edges between the entities. SUBDUE uses the minimum description length (MDL) principle to identify patterns that minimize the number of bits needed to describe the input graph after being compressed by the pattern. SUBDUE can perform several learning tasks, including unsupervised learning, supervised learning, clustering and graph grammar learning. </span>RNNLIB - A recurrent neural network library for sequence learning problems. As published by Marcus Liwickihttps://github.com/mrhichem/RNNLIBgromgull2011-08-12T10:24:52+02:00neural-networks recurrent-neural-networks machine-learning hand-writing-recognition <span itemprop="description">RNNLIB - A recurrent neural network library for sequence learning problems. As published by Marcus Liwicki</span>http://www.slideshare.net/GaelVaroquaux/python-for-brain-mining-neuroscience-with-state-of-the-art-machine-learning-and-data-visualizationgromgull2011-07-15T15:50:41+02:00toread python machine-learning <a itemprop="url" data-versiondate="2011-07-15T15:50:41+02:00" href="http://www.slideshare.net/GaelVaroquaux/python-for-brain-mining-neuroscience-with-state-of-the-art-machine-learning-and-data-visualization" rel="nofollow" class="description-link">http://www.slideshare.net/GaelVaroquaux/python-for-brain-mining-neuroscience-with-state-of-the-art-machine-learning-and-data-visualization</a>http://zyxo.wordpress.com/2010/09/17/why-decision-trees-is-the-best-data-mining-algorithm/gromgull2011-06-14T16:18:58+02:00machine-learning decision-trees <a itemprop="url" data-versiondate="2011-06-14T16:18:58+02:00" href="http://zyxo.wordpress.com/2010/09/17/why-decision-trees-is-the-best-data-mining-algorithm/" rel="nofollow" class="description-link">http://zyxo.wordpress.com/2010/09/17/why-decision-trees-is-the-best-data-mining-algorithm/</a>Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.http://mloss.org/software/view/128/gromgull2011-05-17T16:36:39+02:00machine-learning open-source tools <span itemprop="description">Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.</span>Markov Logic Networks (MLNs) is a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, text mining, and natural language processing. Based on principled data management techniques, Tuffy is an MLN inference engine that achieves scalability and orders of magnitude speedup compared to prior art implementations. It is written in Java and relies on PostgreSQL. For a brief introduction to MLNs and the technical details of Tuffy, please see our technical report. http://research.cs.wisc.edu/hazy/tuffy/gromgull2011-04-21T11:45:53+02:00machine-learning markov-logic-networks java postgresql <span itemprop="description">Markov Logic Networks (MLNs) is a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, text mining, and natural language processing. Based on principled data management techniques, Tuffy is an MLN inference engine that achieves scalability and orders of magnitude speedup compared to prior art implementations. It is written in Java and relies on PostgreSQL. For a brief introduction to MLNs and the technical details of Tuffy, please see our technical report. </span>Local Outlier Factor (LOF) is an anomaly detection algorithm presented as "LOF: Identifying Density-based Local Outliers" by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander[1]. The key idea of LOF is comparing the local density of a point's neighborhood with the local density of its neighbors.http://en.wikipedia.org/wiki/Local_Outlier_Factorgromgull2011-04-19T11:20:06+02:00machine-learning anomaly-detection <span itemprop="description">Local Outlier Factor (LOF) is an anomaly detection algorithm presented as &#034;LOF: Identifying Density-based Local Outliers&#034; by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander[1]. The key idea of LOF is comparing the local density of a point&#039;s neighborhood with the local density of its neighbors.</span>