ISWC 2012 Tutorial - Machine Learning on Linked Data: Tensors and their Applications in Graph-Structured DomainsThis tutorial will provide an introduction to tensor factorizations and their applications for machine learning on graphs.Maximilian Nickel
FactorieFACTORIE 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.
Pattern | CLiPSPattern 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).
Why Apache Giraph is more than a graph processing system | “I for one welcome our new computer overlords”The Apache Giraph project is a fault-tolerant in-memory distributed graph processing system which runs on top of a standard Hadoop installation.
Infer.NETInfer.NET is a framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. You can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification or clustering through to customised solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.