BibSonomy ::  user :: gromgull ::

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.

Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.

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.

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.

In computer science, a kd-tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. kd-trees are a useful data structure for several applications, such as searches involving a multidimensional search key (e.g. range searches and nearest neighbour searches).

A great deal of research has focused on algorithms for learning features from un- labeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning al- gorithms and deep models. In this paper, however, we show that several very sim- ple factors, such as the number of hidden nodes in the model, may be as important to achieving high performance as the choice of learning algorithm or the depth of the model. Specifically, we will apply several off-the-shelf feature learning al- gorithms (sparse auto-encoders, sparse RBMs and K-means clustering, Gaussian mixtures) to NORB and CIFAR datasets using only single-layer networks. We then present a detailed analysis of the effect of changes in the model setup: the receptive field size, number of hidden nodes (features), the step-size (“stride”) be- tween extracted features, and the effect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are as critical to achieving high performance as the choice of algorithm itself—so critical, in fact, that when these parameters are pushed to their limits, we are able to achieve state-of-the- art performance on both CIFAR and NORB using only a single layer of features. More surprisingly, our best performance is based on K-means clustering, which is extremely fast, has no hyper-parameters to tune beyond the model structure it- self, and is very easy implement. Despite the simplicity of our system, we achieve performance beyond all previously published results on the CIFAR-10 and NORB datasets (79.6% and 97.0% accuracy respectively).

The workshop aims to discuss key issues and practices of semantic mining. Thanks to the initiatives of the Linked Open Data and robust techniques for semantic annotation of Web, social, and sensor data, more semantic data is available. Many research efforts have been directed toward demonstrating semantic techniques to analyze and mine this growing resource. The workshop will provide a cross-disciplinary forum for researchers to showcase their innovation and efforts, and to further enhance existing bounds and create new connections among different communities. Here we solicit contributions on researches and practices of mining data semantics including theory, algorithms, and applications from computer science, life science, healthcare and other domains.

Ninth Workshop on Mining and Learning with Graphs will be held in conjunction with the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining that will take place August 21-24, 2011 in San Diego, CA.

Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning licensed under the Mozilla Public License (MPL). We develop an open source machine learning toolkit which provides algorithms for machine learning utilising the power of multi-core/multi-threaded processors/operating systems (Linux, WIndows, Mac OS X), a graphical user interface for users who want to quickly prototype machine learning experiments, tutorials to support learning about Statistical Machine Learning (Statistical Machine Learning at The Australian National University), and detailed and precise documentation for each of the above.

RDF data can be analyzed with various query languages such as SPARQL or SeRQL. Due to their nature these query languages do not support fuzzy queries. In this paper we present a new method that transforms the information presented by subject-relation-object relations within RDF data into Activation Patterns. These patterns represent a common model that is the basis for a number of sophisticated analysis methods such as semantic relation analysis, semantic search queries, unsuper- vised clustering, supervised learning or anomaly detection. In this paper, we explain the Activation Patterns concept and apply it to an RDF representation of the well known CIA World Factbook.

PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library.