Search Results for "learning classifier system"

Elasticsearch is a distributed, RESTful search and analytics engine that lets you store, search and analyze with ease at scale. It lets you perform and combine many types of searches; it scales seamlessly, and offers answers incredibly fast with search results you can rank based on a variety of factors. Elasticsearch can be used for a wide variety of use cases, from maps and metrics to site search and workplace search, and with all data types.

Web pages do not offer reliable metadata concerning their creation date and time. However, getting the document creation time is a necessary step for allowing to apply temporal normalization systems to web pages. DCTFinder is a system that parses a web page and extracts from its content the title and the creation date of this web page. DCTFinder combines heuristic title detection, supervised learning with Conditional Random Fields (CRFs) for document date extraction, and rule-based creation time recognition. DCTFinder is released under CeCILL free software license agreement. ...

This is a GUI application for learning non disjoint groups based on Weka machine learning framework. It offers a variety of learning methods, based on k-means, able to produce overlapping clusters. The application also contains an evaluation framework that calculates several external validation measures. The application offers a visualization tool to discover overlapping groups.

JBoost is a simple, robust system for classification. JBoost contains implementations of several boosting algorithms in an alternating decision tree framework. In addition, JBoost provides extensible software for adding more learning algorithms.

This proyect presents a system, which, from a corpus of documents, extracts information about a theme area, and a pedagogical components collection. This information is packed into fine granularity learning objects (metadata included).

A project aims to develop a system which trains LDA model in distributed enviorenment. I studied Hadoop based solution and found that Hadoop is not fit for distributed LDA training case. In this project I implement a platform based on socket.

HORUS is a system for knowledge acquisition, hypothesis generation, inference and learning. It is an interactive, internet environment accessible to a diverse community of users (public-access or membership basis) - see also UMKAILASH project for more.

ScenConnect shows scenarios as networks of situation and event tag sets, for fast comparisons. It links scenarios to tags, scores, and other metadata, creating situationals suitable for search, mining, machine learning, and planning.