]> BibSonomy publications for /author/Madden http://www.bibsonomy.org/burst/author/Madden BibSonomy BuRST Feed for /author/Madden 2008-07-27T02:39:47+02:00 The Emergence of a Networking Primitive in Wireless Sensor Networks http://www.bibsonomy.org/bibtex/2b3a25be5058b0e6abc211dbf18e809a3/flint63 flint63 2008-07-22T11:53:40+02:00 acm device embedded middleware network paper sensor v0805
The Emergence of a Networking Primitive in Wireless Sensor Networks
Philip Levis and Eric Brewer and David Culler and David Gay and Samuel Madden and Neil Patel and Joe Polastre and Scott Shenker and Robert Szewczyk and Alec Woo Communications of the ACM 51 99-106 (2008)
to acm device embedded middleware network paper sensor v0805 by flint63 on 2008-07-22 11:53:40 ]]> Communications of the ACM799-106The Emergence of a Networking Primitive in Wireless Sensor Networks512008acm device embedded middleware network paper sensor v0805 2008-07-22 11:53:40.0The wireless sensor network community approached networking abstractions as an open question, allowing answers to emerge with time and experience. The Trickle algorithm has become a basic mechanism used in numerous protocols and systems. Trickle brings nodes to eventual consistency quickly and efficiently while remaining remarkably robust to variations in network density, topology, and dynamics. Instead of flooding a network with packets, Trickle uses a polite gossip policy to control send rates so each node hears just enough packets to stay consistent. This simple mechanism enables Trickle to scale to 1000-fold changes in network density, reach consistency in seconds, and require only a few bytes of state yet impose a maintenance cost of a few sends an hour. Originally designed for disseminating new code, experience has shown Trickle to have much broader applicability, including route maintenance and neighbor discovery. This paper provides an overview of the research challenges wireless sensor networks face, describes the Trickle algorithm, and outlines several ways it is used today. Information Survival Threshold in Sensor and P2P Networks. dblphttp://www.bibsonomy.org/bibtex/21822b44a843831c4a9725714eb8135d9/anaappel anaappel 2008-07-09T20:29:47+02:00 Survival Threshold networks.
Information Survival Threshold in Sensor and P2P Networks.
Deepayan Chakrabarti and Jure Leskovec and Christos Faloutsos and Samuel Madden and Carlos Guestrin and Michalis Faloutsos INFOCOM 1316-1324 (2007)
to Survival Threshold networks. by anaappel on 2008-07-09 20:29:47 ]]> INFOCOMconf/infocom/20071316-1324Information Survival Threshold in Sensor and P2P Networks.2007Survival Threshold networks. 2008-07-09 20:29:47.0 The Genetic Kernel Support Vector Machine: Description and Evaluation http://www.bibsonomy.org/bibtex/2274c31a292dbfd212858667cfc3a5a7f/brazovayeye brazovayeye 2008-06-19T17:35:00+02:00 Kernel Kernel, Mercer SVM, algorithms, classification, genetic machine model programming, selection, support vector
The Genetic Kernel Support Vector Machine: Description and Evaluation
Tom Howley and Michael G. Madden Artificial Intelligence Review 24 379--395 (2005)
to Kernel Kernel, Mercer SVM, algorithms, classification, genetic machine model programming, selection, support vector by brazovayeye on 2008-06-19 17:35:00 ]]> Artificial Intelligence Review3-4379--395The Genetic Kernel Support Vector Machine: Description and Evaluation242005Kernel Kernel, Mercer SVM, algorithms, classification, genetic machine model programming, selection, support vector 2008-06-19 17:35:00.0The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid kernel with various parameter settings An improved genetic programming technique for the classification of Raman spectra http://www.bibsonomy.org/bibtex/20b2cd926368996ea6de3dc226f5c17cb/brazovayeye brazovayeye 2008-06-19T17:35:00+02:00 Machine Neural Raman Spectroscopy, algorithms, genetic learning, networks, programming,
An improved genetic programming technique for the classification of Raman spectra
Kenneth Hennessy and Michael G. Madden and Jennifer Conroy and Alan G. Ryder Knowledge Based Systems 18 217--224 (2005)
to Machine Neural Raman Spectroscopy, algorithms, genetic learning, networks, programming, by brazovayeye on 2008-06-19 17:35:00 ]]> Knowledge Based SystemsAugustAI-2004, Cambridge, England, 13th-15th December 20044-5217--224An improved genetic programming technique for the classification of Raman spectra182005Machine Neural Raman Spectroscopy, algorithms, genetic learning, networks, programming, 2008-06-19 17:35:00.0The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the certainty levels associated with discovered rules, so as to minimize the chance of misclassification of future samples. An Axiomatic Comparison of Learned Term-weighting Schemes in Information Retrieval http://www.bibsonomy.org/bibtex/2856f41e0d2e98bc0612140d9f12de30a/brazovayeye brazovayeye 2008-06-19T17:35:00+02:00 algorithms, genetic programming
An Axiomatic Comparison of Learned Term-weighting Schemes in Information Retrieval
Ronan Cummins and Colm O'Riordan 18th Irish Conference on Artificial Intelligence and Cognitive Science (2007)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:35:00 ]]> Dublin Institute of Technology18th Irish Conference on Artificial Intelligence and Cognitive Science29-31 AugustAn Axiomatic Comparison of Learned Term-weighting Schemes in Information Retrieval2007algorithms, genetic programming 2008-06-19 17:35:00.0 The pothole patrol: using a mobile sensor network for road surface monitoring. dblphttp://www.bibsonomy.org/bibtex/24d87922bad1f2ee9f56a8bd03f5a8659/dblp dblp 2008-06-19T00:00:00+02:00 dblp
The pothole patrol: using a mobile sensor network for road surface monitoring.
Jakob Eriksson and Lewis Girod and Bret Hull and Ryan Newton and Samuel Madden and Hari Balakrishnan MobiSys 29-39 (2008)
to dblp by dblp on 2008-06-19 00:00:00 ]]> MobiSysconf/mobisys/200829-39The pothole patrol: using a mobile sensor network for road surface monitoring.2008dblp 2008-06-19 00:00:00.0 Data mining of search engine logs http://www.bibsonomy.org/bibtex/22498b2d6fd1e85bda16138915a7337e6/pprett pprett 2008-06-17T16:01:02+02:00 log, mining, query, transformation
Data mining of search engine logs
Martin Whittle and Barry Eaglestone and Nigel Ford and Valerie J. Gillet and Andrew Madden Journal of the American Society for Information Science and Technology 9999 NA+ (2007)
to log, mining, query, transformation by pprett on 2008-06-17 16:01:02 ]]> Department of Information Studies, University of Sheffield, UKJournal of the American Society for Information Science and Technology9999NA+Data mining of search engine logs99992007log, mining, query, transformation 2008-06-17 16:01:02.0This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of ?query transformation?. It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web-based searching by the general public. Design and evaluation of a compiler for embedded stream programs. dblphttp://www.bibsonomy.org/bibtex/2172dd21881da72ab6f8dc918aa220db3/dblp dblp 2008-06-16T00:00:00+02:00 dblp
Design and evaluation of a compiler for embedded stream programs.
Ryan Newton and Lewis Girod and Michael B. Craig and Samuel Madden and J. Gregory Morrisett LCTES 131-140 (2008)
to dblp by dblp on 2008-06-16 00:00:00 ]]> LCTESconf/lctrts/2008131-140Design and evaluation of a compiler for embedded stream programs.2008dblp 2008-06-16 00:00:00.0 Computer technology students - what skills do they really need? dblphttp://www.bibsonomy.org/bibtex/25ed758388c59f982b70928633e44a3b7/dblp dblp 2008-06-11T00:00:00+02:00 dblp
Computer technology students - what skills do they really need?
Elizabeth Haywood and Jane Madden ACSE 139-144 (2000)
to dblp by dblp on 2008-06-11 00:00:00 ]]> ACSEconf/acse/2000139-144ACM International Conference Proceeding SeriesComputer technology students - what skills do they really need?82000dblp 2008-06-11 00:00:00.0 OLTP through the looking glass, and what we found there. dblphttp://www.bibsonomy.org/bibtex/246d1083ce00e235a73475231eef34a74/dblp dblp 2008-06-10T00:00:00+02:00 dblp
OLTP through the looking glass, and what we found there.
Stavros Harizopoulos and Daniel J. Abadi and Samuel Madden and Michael Stonebraker SIGMOD Conference 981-992 (2008)
to dblp by dblp on 2008-06-10 00:00:00 ]]> SIGMOD Conferenceconf/sigmod/2008981-992OLTP through the looking glass, and what we found there.2008dblp 2008-06-10 00:00:00.0