BibSonomy publications for /author/MitchellTue Feb 14 00:00:00 CET 2012Perform. Eval.9-10469-487Analytical models for understanding misbehavior and MAC friendliness in CSMA networks.662009dblp Tue Feb 14 00:00:00 CET 2012WCNCconf/wcnc/20073996-4001A Single Node Decomposition Based Analytic Model for Multiclass Route Optimization in Wireless Mesh Networks.2007dblp Tue Feb 14 00:00:00 CET 2012WCNCconf/wcnc/2007355-360Misbehavior and MAC Friendliness in CSMA Networks.2007dblp Tue Feb 14 00:00:00 CET 2012PE-WASUNconf/pe-wasun/2011117-120Competition, cooperation, and optimization in Multi-Hop CSMA networks.2011dblp Mon Feb 13 15:23:25 CET 2012Algorithmica3386--414Delineating Boundaries for Imprecise Regions502008red-blue_separation myown minimum_perimeter_polygon imprecise_regions GIS In geographic information retrieval, queries often utilize names of geographic regions that do not have a well-defined boundary, such as ``Southern France.'' We provide two classes of algorithms for the problem of computing reasonable boundaries of such regions, based on evidence of given data points that are deemed likely to lie either inside or outside the region. Our problem formulation leads to a number of problems related to red-blue point separation and minimum-perimeter polygons, many of which we solve algorithmically. We give experimental results from our implementation and a comparison of the two approaches.Mon Feb 13 15:23:25 CET 2012Proc. 13th Annu. Europ. Symp. on Algorithms (ESA'05)143--154Lecture Notes in Computer ScienceDelineating Boundaries for Imprecise Regions36692005myown Mon Feb 13 00:00:00 CET 2012IEEE Communications Magazine1090-97Networking and application interface technology for wireless sensor network surveillance and monitoring.492011dblp Mon Feb 13 00:00:00 CET 2012IEEE Communications Letters4262-263Comments on mutual authentication and key exchange protocols for low power wireless communications.82004dblp Mon Feb 13 00:00:00 CET 2012WSDMconf/wsdm/201273-82Coupled temporal scoping of relational facts.2012dblp Sat Feb 11 00:00:00 CET 2012IEEE Communications Letters2178-180Reliable control protocol for crossbar arbitration.92005dblp Fri Feb 10 00:00:00 CET 2012IEEE Transactions on Information Theory2530-547Rateless Coding for Gaussian Channels.582012dblp Thu Feb 09 00:00:00 CET 2012IEEE Transactions on Signal Processing3759-763Robust and accurate ARX and ARMA model order estimation of non-Gaussian processes.502002dblp Wed Feb 08 00:00:00 CET 2012Security and Communication Networks187-106Generating certification authority authenticated public keys in ad hoc networks.52012dblp Wed Feb 08 00:00:00 CET 2012CCCGconf/cccg/2011Probabilistic Bounds on the Length of a Longest Edge in Delaunay Graphs of Random Points in d-Dimensions.2011dblp Wed Feb 08 00:00:00 CET 2012ITCconf/itc/1998480-486Diagnostic techniques for the UltraSPARC microprocessors.1998dblp Tue Feb 07 00:00:00 CET 2012ITCconf/itc/200510Production-oriented interface testing for PCI-Express by enhanced loop-back technique.2005dblp Tue Feb 07 00:00:00 CET 2012ITCconf/itc/20061-10Testable Design for Adaptive Linear Equalizer in High-Speed Serial Links.2006dblp Mon Feb 06 00:00:00 CET 2012Multiscale Modeling & Simulation41495-1527Analysis of the Quasi-Nonlocal Approximation of Linear and Circular Chains in the Plane.92011dblp Thu Feb 02 16:13:22 CET 2012Artificial Intelligence1–269 - 113Learning to construct knowledge bases from the World Wide Web1182000ai bachelor:2011:bachmann naiveBayes webclassificatoion www The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer understandable knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more effective retrieval of Web information, and promote new uses of the Web to support knowledge-based inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs. The first is an ontology that defines the classes (e.g., company, person, employee, product) and relations (e.g., employed_by, produced_by) of interest when creating the knowledge base. The second is a set of training data consisting of labeled regions of hypertext that represent instances of these classes and relations. Given these inputs, the system learns to extract information from other pages and hyperlinks on the Web. This article describes our general approach, several machine learning algorithms for this task, and promising initial results with a prototype system that has created a knowledge base describing university people, courses, and research projects.Thu Feb 02 00:00:00 CET 2012Electronic Colloquium on Computational Complexity (ECCC)012The Resolution Complexity of Random Graph k-Colorability2004dblp