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Instance-based learning algorithms
- Machine Learning
, 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
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Cited by 1389 (18 self)
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databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a
A comparison of mechanisms for improving TCP performance over wireless links
- IEEE/ACM TRANSACTIONS ON NETWORKING
, 1997
"... Reliable transport protocols such as TCP are tuned to perform well in traditional networks where packet losses occur mostly because of congestion. However, networks with wireless and other lossy links also suffer from significant losses due to bit errors and handoffs. TCP responds to all losses by i ..."
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Cited by 927 (11 self)
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by invoking congestion control and avoidance algorithms, resulting in degraded end-to-end performance in wireless and lossy systems. In this paper, we compare several schemes designed to improve the performance of TCP in such networks. We classify these schemes into three broad categories: end
A Scalable Location Service for Geographic Ad Hoc Routing,”
- Proceedings of ACM/IEEE MobiCom
, 2000
"... Abstract. GLS is a new distributed location service which tracks mobile node locations. GLS combined with geographic forwarding allows the construction of ad hoc mobile networks that scale to a larger number of nodes than possible with previous work. GLS is decentralized and runs on the mobile node ..."
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Cited by 769 (17 self)
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requirements of GLS grow slowly with the size of the network. Furthermore, GLS tolerates node failures well: each failure has only a limited effect and query performance degrades gracefully as nodes fail and restart. The query performance of GLS is also relatively insensitive to node speeds. Simple geographic
Performance Degradation Analysis of a Supercomputer
"... Abstract—We analyze performance degradation phenomena due to software aging on a real supercomputer deployed at the Federico II University of Naples, by considering a dataset of ten months of operational usage. We adopted a statistical approach for identifying when and where the supercomputer experi ..."
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Abstract—We analyze performance degradation phenomena due to software aging on a real supercomputer deployed at the Federico II University of Naples, by considering a dataset of ten months of operational usage. We adopted a statistical approach for identifying when and where the supercomputer
Understanding the Backward Slices of Performance Degrading Instructions
- in Proceedings of the 27th Annual International Symposium on Computer Architecture
, 2000
"... For many applications, branch mispredictions and cache misses limit a processor's performance to a level well below its peak instruction throughput. A small fraction of static instructions, whose behavior cannot be anticipated using current branch predictors and caches, contribute a large fract ..."
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Cited by 85 (3 self)
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fraction of such performance degrading events. This paper analyzes the dynamic instruction stream leading up to these performance degrading instructions to identify the operations necessary to execute them early. The backward slice (the subset of the program that relates to the instruction
Statistical phrase-based translation
, 2003
"... We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outpe ..."
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Cited by 944 (11 self)
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. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems. 1
Supervised and unsupervised discretization of continuous features
- in A. Prieditis & S. Russell, eds, Machine Learning: Proceedings of the Twelfth International Conference
, 1995
"... Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de n-ing characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised dis ..."
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Cited by 540 (11 self)
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-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm signi cantly improved if features were discretized in advance � in our experiments, the performance never signi cantly degraded, an interesting phenomenon considering the fact that C4
Fine-grained Mobility in the Emerald System
- ACM Transactions on Computer Systems
, 1988
"... Emerald is an object-based language and system designed for the construction of distributed programs. An explicit goal of Emerald is support for object mobility; objects in Emerald can freely move within the system to take advantage of distribution and dynamically changing environments. We say that ..."
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Cited by 546 (23 self)
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and run-time mechanisms that support mobility, and techniques for implementing mobility that do not degrade the performance of local operations. Performance measurements of the current implementation are included.
Parallel Networks that Learn to Pronounce English Text
- COMPLEX SYSTEMS
, 1987
"... This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed h ..."
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Cited by 549 (5 self)
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human performance. (i) The learning follows a power law. (;i) The more words the network learns, the better it is at generalizing and correctly pronouncing new words, (iii) The performance of the network degrades very slowly as connections in the network are damaged: no single link or processing unit
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
- IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
, 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
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Cited by 539 (17 self)
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of applications, often being significantly faster (in terms of computation time) than competing methods. Although the performance of GP methods tends to degrade as the regularization term is de-emphasized, we show how they can be embedded in a continuation scheme to recover their efficient practical performance.
Results 1 - 10
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