• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 13,394
Next 10 →

A Classification of Ontology Change

by Giorgos Flouris, Dimitris Plexousakis, Grigoris Antoniou
"... Abstract — The problem of modifying an ontology in response to a certain need for change is a complex and multifaceted one, being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear, as ce ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
Abstract — The problem of modifying an ontology in response to a certain need for change is a complex and multifaceted one, being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear

Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce

by Dieter Fensel , 2007
"... Currently computers are changing from single isolated devices to entry points into a world wide network of information exchange and business transactions called the World Wide Web (WWW). Therefore support in the exchange of data, information, and knowledge exchange is becoming the key issue in cur ..."
Abstract - Cited by 656 (45 self) - Add to MetaCart
Currently computers are changing from single isolated devices to entry points into a world wide network of information exchange and business transactions called the World Wide Web (WWW). Therefore support in the exchange of data, information, and knowledge exchange is becoming the key issue

Imagenet: A large-scale hierarchical image database

by Jia Deng, Wei Dong, Richard Socher, Li-jia Li, Kai Li, Li Fei-fei - In CVPR , 2009
"... The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce her ..."
Abstract - Cited by 840 (28 self) - Add to MetaCart
here a new database called “ImageNet”, a largescale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions

Working Knowledge

by Thomas Davenport, Laurence Prusak, Gary Wills, Harith Alani, Ronald Ashri, Richard Crowder, Yannis Kalfoglou, Sanghee Kim , 1998
"... While knowledge is viewed by many as an asset, it is often difficult to locate particular items within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work ..."
Abstract - Cited by 527 (0 self) - Add to MetaCart
While knowledge is viewed by many as an asset, it is often difficult to locate particular items within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work

Bagging predictors

by LEO BREIMAN , 1996
"... Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making ..."
Abstract - Cited by 3650 (1 self) - Add to MetaCart
Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed

Attention, similarity, and the identification-Categorization Relationship

by Robert M. Nosofsky , 1986
"... A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification dat ..."
Abstract - Cited by 690 (28 self) - Add to MetaCart
A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification

A classification of schema-based matching approaches

by Pavel Shvaiko - JOURNAL ON DATA SEMANTICS , 2005
"... Schema/ontology matching is a critical problem in many application domains, such as, semantic web, schema/ontology integration, data warehouses, e-commerce, catalog matching, etc. Many diverse solutions to the matching problem have been proposed so far. In this paper we present a taxonomy of schema- ..."
Abstract - Cited by 386 (21 self) - Add to MetaCart
Schema/ontology matching is a critical problem in many application domains, such as, semantic web, schema/ontology integration, data warehouses, e-commerce, catalog matching, etc. Many diverse solutions to the matching problem have been proposed so far. In this paper we present a taxonomy of schema

MetaCost: A General Method for Making Classifiers Cost-Sensitive

by Pedro Domingos - In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining , 1999
"... Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob- lems. Individually making each classification learner costsensi ..."
Abstract - Cited by 415 (4 self) - Add to MetaCart
Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob- lems. Individually making each classification learner

The Foundations of Cost-Sensitive Learning

by Charles Elkan - In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence , 2001
"... This paper revisits the problem of optimal learning and decision-making when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrix is reasonable, and we show how to avoid the mistake of defining a cost matrix that is economically i ..."
Abstract - Cited by 402 (6 self) - Add to MetaCart
incoherent. For the two-class case, we prove a theorem that shows how to change the proportion of negative examples in a training set in order to make optimal cost-sensitive classification decisions using a classifier learned by a standard non-costsensitive learning method. However, we then argue

The structure of phenotypic personality traits

by Lewis R. Goldberg - American Psychologist , 1993
"... This personal historical article traces the development of the Big-Five factor structure, whose growing acceptance by personality researchers has profoundly influenced the scientific study of individual differences. The roots of this taxonomy lie in the lexical hypothesis and the insights of Sir Fra ..."
Abstract - Cited by 383 (3 self) - Add to MetaCart
of attempts to assimilate other models into the five-factor structure. Lately, some practical im-plications of the emerging consensus can be seen in such contexts as personnel selection and classification. Once upon a time, we had no personalities (Mi-schel, 1968). Fortunately times change, and thepast decade
Next 10 →
Results 1 - 10 of 13,394
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University