IEEE Intelligent Systems & Their Applications 

About: IEEE Intelligent Systems & Their Applications is an academic journal. The journal publishes majorly in the area(s): The Internet & Knowledge-based systems. It has an ISSN identifier of 1094-7167. Over the lifetime, 221 publications have been published receiving 22242 citations. The journal is also known as: IEEE Intell Syst.

Support vector machines

Abstract: My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

What are ontologies, and why do we need them?

Abstract: This survey provides a conceptual introduction to ontologies and their role in information systems and AI. The authors also discuss how ontologies clarify the domain's structure of knowledge and enable knowledge sharing.

Feature subset selection using a genetic algorithm

Abstract: Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.

An architecture-based approach to self-adaptive software

Abstract: Self-adaptive software requires high dependability robustness, adaptability, and availability. The article describes an infrastructure supporting two simultaneous processes in self-adaptive software: system evolution, the consistent application of change over time, and system adaptation, the cycle of detecting changing circumstances and planning and deploying responsive modifications.

OntoSeek: content-based access to the Web

Abstract: For online yellow pages and product catalogs, structured content representations coupled with linguistic ontologies can increase both recall and precision of content based retrieval. Our OntoSeek system adopts a language of limited expressiveness for content representation and uses a large ontology based on WordNet for content matching. WordNet is a linguistic database formed by synsets-terms grouped into semantic equivalence sets, each one assigned to a lexical category (noun, verb, adverb, adjective). Each synset represents a particular sense of an English word and is usually expressed as a unique combination of synonymous words.