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

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 21,793
Next 10 →

Understanding packet delivery performance in dense wireless sensor networks

by Jerry Zhao , 2003
"... Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery ..."
Abstract - Cited by 661 (15 self) - Add to MetaCart
Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery

Design and Evaluation of a Compiler Algorithm for Prefetching

by Todd C. Mowry, Monica S. Lam, Anoop Gupta - in Proceedings of the Fifth International Conference on Architectural Support for Programming Languages and Operating Systems , 1992
"... Software-controlled data prefetching is a promising technique for improving the performance of the memory subsystem to match today's high-performance processors. While prefetching is useful in hiding the latency, issuing prefetches incurs an instruction overhead and can increase the load on the ..."
Abstract - Cited by 501 (20 self) - Add to MetaCart
Software-controlled data prefetching is a promising technique for improving the performance of the memory subsystem to match today's high-performance processors. While prefetching is useful in hiding the latency, issuing prefetches incurs an instruction overhead and can increase the load

A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm

by Martin Riedmiller, Heinrich Braun - IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS , 1993
"... A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behaviour of the errorfunction. In substantial difference to other adaptive tech ..."
Abstract - Cited by 938 (34 self) - Add to MetaCart
A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behaviour of the errorfunction. In substantial difference to other adaptive

Local features and kernels for classification of texture and object categories: a comprehensive study

by J. Zhang, S. Lazebnik, C. Schmid - International Journal of Computer Vision , 2007
"... Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations an ..."
Abstract - Cited by 653 (34 self) - Add to MetaCart
Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations

Markov Logic Networks

by Matthew Richardson, Pedro Domingos - MACHINE LEARNING , 2006
"... We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
Abstract - Cited by 816 (39 self) - Add to MetaCart
in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently

SPEA2: Improving the Strength Pareto Evolutionary Algorithm

by Eckart Zitzler, Marco Laumanns, Lothar Thiele , 2001
"... The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very ..."
Abstract - Cited by 708 (19 self) - Add to MetaCart
very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference in various recent investigations, e.g., (Corne, Knowles, and Oates 2000). Furthermore, it has been used in different applications, e.g., (Lahanas, Milickovic, Baltas

Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics

by Shao-en Ong, Blagoy Blagoev, Irina Kratchmarova, Dan Bach Kristensen, Akhilesh P, Matthias Mann - Mol. Cell. Proteomics , 2002
"... The abbreviations used are: SILAC: Stable isotope labeling by amino acids in cell culture, 2DE: two dimensional (isoelectric focusing/SDS-PAGE) gel electrophoresis: ICATTM: isotope-coded affinity tag; MS: mass spectrometry; MALDI-TOF: matrix assisted laser desorption ionization-time of flight; PMF: ..."
Abstract - Cited by 595 (23 self) - Add to MetaCart
: peptide mass fingerprinting; LC-MS: liquid chromatography-MS; Copyright 2002 by The American Society for Biochemistry and Molecular Biology, Inc. Quantitative proteomics has traditionally been performed by 2D gel electrophoresis but recently, mass spectrometric methods based on stable isotope quantitation

Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy

by Hanchuan Peng, Fuhui Long, Chris Ding - IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2005
"... Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first der ..."
Abstract - Cited by 571 (8 self) - Add to MetaCart
compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI

Equation-based congestion control for unicast applications

by Sally Floyd , Mark Handley , Jitendra Padhye , Jörg Widmer - SIGCOMM '00 , 2000
"... This paper proposes a mechanism for equation-based congestion control for unicast traffic. Most best-effort traffic in the current Internet is well-served by the dominant transport protocol, TCP. However, traffic such as best-effort unicast streaming multimedia could find use for a TCP-friendly cong ..."
Abstract - Cited by 830 (29 self) - Add to MetaCart
single round-trip time. We use both simulations and experiments over the Internet to explore performance. We consider equation-based congestion control a promising avenue of development for congestion control of multicast traffic, and so an additional motivation for this work is to lay a sound basis

Data Mining: Concepts and Techniques

by Jiawei Han, Micheline Kamber , 2000
"... Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientific and government transactions and managements, a ..."
Abstract - Cited by 3142 (23 self) - Add to MetaCart
, a promising and flourishing frontier in database systems and new database applications. Data mining, also popularly referred to as knowledge discovery in databases (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored in large databases, data
Next 10 →
Results 1 - 10 of 21,793
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