This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting,
Scalable and Efficient Data Streaming Algorithms for Detecting Common Content in Internet Traffic. Minho Sung, Abhishek Kumar, Li Li, Jia Wang, Jun Xu. To appear in the Proc. of 2nd IEEE International Workshop on Networking Meets Databases (NetDB'06), April 2006. Sketch Guided Sampling -- Using On-Line Estimates of Flow Size for Adaptive Data Collection. Abhishek Kumar, Jun (Jim) Xu. To appear in the proceedings of IEEE Infocom'06, Barcelona, Spain, April 2006.
Xstructure is a service for browsing and searching papers in arxiv.org
Among the features of this service are:
* Automated generation of hierarchical classification scheme for the papers. The scheme results from classification of the papers in the arxiv database. The only input for the classification is the citation graph. The number of the levels in the hierarchy and the number of the clusters is determined by the algorithm. The algorithm creates the classification scheme, and indexes the papers by the created classification;
* The classification is used to index the new papers. We plan to rebuild the classification scheme regularly. In this way, we will take into account that appearance of new papers may lead to emergence of new themes. Detection of new themes is one of our objectives;
* A number of extra attributes (e.g. Theme name, Authority and Review Articles, etc.) for the elements (themes) of the classification (see Help);
* Accessability of the classification in response to search requests via display options, e.g., display as Tree of Themes, and Refrerence (Citation) Tree.
About 10% of papers from arxiv are missed in our database. We work on decreasing this number.
Comments, questions, and suggestions are to be sent to Grigorii Pivovarov
The aim of the International Journal of Advances in Internet of Things is to provide a forum for scientists and social workers to present and discuss issues in the impact of the Internet to the society and disseminate findings in scientific research on related subjects.
Database of animal natural history, distribution, classification, and conservation biology. Contains species accounts about individual animal species and descriptions of levels of organization above the species level, especially phyla, classes, and in some cases, orders and families.
People have been trying to classify and organize information for thousands of years. There are many examples of cataloged items in ancient repositories, including items in the Library of Alexandria in Egypt. Taxonomy arose as an attempt to organize inform
The British Classification Society exists to encourage the co-operation and exchange of views and information among those interested in principles and practice of classification in any discipline where they are used. Its membership includes anthropologists, archaeologists, astronomers, biologists, chemists, computer scientists, forensic scientists, geologists, information specialists, librarians, psychologists, soil scientists and statisticians. The Society organises meetings, some by itself, but often jointly with societies representing application areas for classification.
The New Opportunities Made Possible Through Ease of Access to Scholarly and Research Data and Information
Summary: The ever-greater online availability of data and information is tending to what we might characterize as syntactic completeness. That is to say, we have an ever-better knowledge of all possible data and ensuing information in the particular domain of scholarly research. In this position statement, we note how the mathematical and computational modelling of data and information are crucial for semantic completeness. Use of data and information implies a well-elaborated understanding of both their syntax and their semantics. More transparent and hence better quality evaluation of both product and process is made possible.
The Cataloger's Reference Shelf is based on 21 MARC manuals and other reference works published by The Library of Congress and frequently accessed by technical services staff. A must see for catalogers!
The Cataloging Lab is a place for catalogers and anyone who cares about library metadata to experiment with creating better controlled vocabularies. Suggesting additions and changes to the Library of Congress Subject Headings vocabulary can be an isolating endeavor—it can be difficult to determine if your heading has already been proposed or if someone else is working on a proposal at the same time you are. The Cataloging Lab is designed to be a wiki where folks can collaborate on headings together to create stronger proposals.
This project contains Naive and Fishers bayesian classifiers, as described in Toby Segaran's book "Programming Collective Intelligence." The book has python implementations; this is a Java implementation.
Welcome to the home page of the Classification Society of North America (CSNA). The CSNA is a nonprofit interdisciplinary organization whose purposes are to promote the scientific study of classification and clustering (including systematic methods of creating classifications from data), and to disseminate scientific and educational information related to its fields of interests.
Social tagging, which is also known as collaborative tagging, social classification, and social indexing, allows ordinary users to assign keywords, or tags, to items.
Concept mining is a discipline at the nexus of data mining, text mining, and linguistics, drawing on artificial intelligence and statistics. It aims to extract concepts from documents.
S. Wu, J. Hofman, W. Mason, und D. Watts. Proceedings of the 20th international conference on World wide web, Seite 705--714. New York, NY, USA, ACM, (2011)
J. Hopcroft, T. Lou, und J. Tang. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Seite 1137--1146. New York, NY, USA, ACM, (2011)