Reliable broadband communication using a burst erasure correcting code
SIGCOMM '90 Proceedings of the ACM symposium on Communications architectures & protocols
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Effective erasure codes for reliable computer communication protocols
ACM SIGCOMM Computer Communication Review
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Multidimensional curve classification using passing—through regions
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Hierarchical Discriminant Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data bubbles: quality preserving performance boosting for hierarchical clustering
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Generalized clustering, supervised learning, and data assignment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Algorithms
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A Study of Adaptive Forward Error Correction for Wireless Collaborative Computing
IEEE Transactions on Parallel and Distributed Systems
Machine Learning
The Vision of Autonomic Computing
Computer
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Hierarchical model-based clustering of large datasets through fractionation and refractionation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Architecture and Operation of an Adaptable Communication Substrate
FTDCS '03 Proceedings of the The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems
Statistical Imitative Learning from Perceptual Data
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Learning Movement Sequences from Demonstration
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Developmental Learning of Memory-Based Perceptual Models
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Concept acquisition through representational adjustment
Concept acquisition through representational adjustment
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Assessment and pruning of hierarchical model based clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Computer
A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
IEEE Transactions on Signal Processing
Composable proxy services to support collaboration on the mobile Internet
IEEE Transactions on Computers
Automatically generating adaptive logic to balance non-functional tradeoffs during reconfiguration
Proceedings of the 7th international conference on Autonomic computing
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Autonomic computing systems must be able to detect and respond to errant behavior or changing conditions with little or no human intervention. Clearly, decision making is a critical issue in such systems, which must learn how and when to invoke corrective actions based on past experience. This paper describes the design, implementation, and evaluation of MESO, a pattern classifier designed to support online, incremental learning and decision making in autonomic systems. A novel feature of MESO is its use of small agglomerative clusters, called sensitivity spheres, that aggregate similar training samples. Sensitivity spheres are partitioned into sets during the construction of a memory-efficient hierarchical data structure. This structure facilitates data compression, which is important to many autonomic systems. Results are presented demonstrating that MESO achieves high accuracy while enabling rapid incremental training and classification. A case study is described in which MESO enables a mobile computing application to learn, by imitation, user preferences for balancing wireless network packet loss and bandwidth consumption. Once trained, the application can autonomously adjust error control parameters as needed while the user roams about a wireless cell.