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Data Sniffing – Monitoring of Machine Learning for Online Adaptive Systems

by Yan Liu, Tim Menzies, Bojan Cukic - 14th IEEE International Conference on Tools with Artificial Intelligence
"... Adaptive systems are systems whose function evolves while adapting to current environmental conditions. Due to the real-time adaptation, newly learned data have a significant impact on system behavior. When online adaptation is included in system control, anomalies could cause abrupt loss of system ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
Adaptive systems are systems whose function evolves while adapting to current environmental conditions. Due to the real-time adaptation, newly learned data have a significant impact on system behavior. When online adaptation is included in system control, anomalies could cause abrupt loss of system

Machine Learning in Automated Text Categorization

by Fabrizio Sebastiani - ACM COMPUTING SURVEYS , 2002
"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."
Abstract - Cited by 1658 (22 self) - Add to MetaCart
to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual

Sparse Bayesian Learning and the Relevance Vector Machine

by Michael E. Tipping, Alex Smola , 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vec ..."
Abstract - Cited by 958 (5 self) - Add to MetaCart
vector machine' (RVM), a model of identical functional form to the popular and state-of-the-art `support vector machine' (SVM). We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer

Live Migration of Virtual Machines

by Christopher Clark, Keir Fraser, Steven H, Jakob Gorm Hansen, Eric Jul, Christian Limpach, Ian Pratt, Andrew Warfield - In Proceedings of the 2nd ACM/USENIX Symposium on Networked Systems Design and Implementation (NSDI , 2005
"... Migrating operating system instances across distinct physical hosts is a useful tool for administrators of data centers and clusters: It allows a clean separation between hardware and software, and facilitates fault management, load balancing, and low-level system maintenance. By carrying out the ma ..."
Abstract - Cited by 613 (14 self) - Add to MetaCart
Migrating operating system instances across distinct physical hosts is a useful tool for administrators of data centers and clusters: It allows a clean separation between hardware and software, and facilitates fault management, load balancing, and low-level system maintenance. By carrying out

The Elements of Statistical Learning -- Data Mining, Inference, and Prediction

by Trevor Hastie, Robert Tibshirani, Jerome Friedman
"... ..."
Abstract - Cited by 1320 (13 self) - Add to MetaCart
Abstract not found

Maté: A Tiny Virtual Machine for Sensor Networks

by Philip Levis, David Culler , 2002
"... Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopu-late. ..."
Abstract - Cited by 502 (21 self) - Add to MetaCart
Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopu

WordNet: An on-line lexical database

by George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, Katherine Miller - International Journal of Lexicography , 1990
"... WordNet is an on-line lexical reference system whose design is inspired by current ..."
Abstract - Cited by 1945 (9 self) - Add to MetaCart
WordNet is an on-line lexical reference system whose design is inspired by current

From data mining to knowledge discovery in databases

by Usama Fayyad, Gregory Piatetsky-shapiro, Padhraic Smyth - AI Magazine , 1996
"... ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases ..."
Abstract - Cited by 510 (0 self) - Add to MetaCart
in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future

Locally weighted learning

by Christopher G. Atkeson, Andrew W. Moore , Stefan Schaal - ARTIFICIAL INTELLIGENCE REVIEW , 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract - Cited by 594 (53 self) - Add to MetaCart
, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted

Bigtable: A distributed storage system for structured data

by Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber - IN PROCEEDINGS OF THE 7TH CONFERENCE ON USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION - VOLUME 7 , 2006
"... Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications ..."
Abstract - Cited by 995 (3 self) - Add to MetaCart
Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications
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