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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11807 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Data Redistribution Algorithms For Heterogeneous Processor Rings
, 2004
"... We consider the problem of redistributing data on homogeneous and heterogeneous ring of processors. The problem arises in several applications, each time after that a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim at op ..."
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Cited by 7 (5 self)
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We consider the problem of redistributing data on homogeneous and heterogeneous ring of processors. The problem arises in several applications, each time after that a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim
31DATA REDISTRIBUTION ON RINGS DATA REDISTRIBUTION ALGORITHMS FOR HETEROGENEOUS PROCESSOR RINGS
"... We consider the problem of redistributing data on homogeneous and heterogeneous rings of processors. The problem arises in several applications, after each invocation of a loadbalancing mechanism (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim at optim ..."
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We consider the problem of redistributing data on homogeneous and heterogeneous rings of processors. The problem arises in several applications, after each invocation of a loadbalancing mechanism (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim
Data Redistribution Algorithms for Homogeneous and Heterogeneous Processor Rings
 in &quot;International Conference on High Performance Computing HiPC’2004&quot;, LNCS
, 2004
"... We consider the problem of redistributing data on homogeneous and heterogeneous processor rings. The problem arises in several applications, each time after a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim at optimi ..."
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Cited by 3 (2 self)
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We consider the problem of redistributing data on homogeneous and heterogeneous processor rings. The problem arises in several applications, each time after a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim
Data redistribution algorithms for heterogeneous processor rings
, 2004
"... We consider the problem of redistributing data on homogeneous and heterogeneous ring of processors. The problem arises in several applications, each time after that a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim at op ..."
Abstract
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We consider the problem of redistributing data on homogeneous and heterogeneous ring of processors. The problem arises in several applications, each time after that a loadbalancing mechanism is invoked (but we do not discuss the loadbalancing mechanism itself). We provide algorithms that aim
Data Streams: Algorithms and Applications
, 2005
"... In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerg ..."
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Cited by 538 (22 self)
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In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has
Analysis of Recommendation Algorithms for ECommerce
, 2000
"... Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations during a live customer interaction and they are achieving widespread success in ECommerce nowadays. In this paper, we investigate several techniques for analyzing largescale pu ..."
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Cited by 515 (26 self)
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scale purchase and preference data for the purpose of producing useful recommendations to customers. In particular, we apply a collection of algorithms such as traditional data mining, nearestneighbor collaborative ltering, and dimensionality reduction on two dierent data sets. The rst data set was derived from
A data locality optimizing algorithm
, 1991
"... 1 Introduction As processor speed continues to increase faster than memory speed, optimizations to use the memory hierarchy efficiently become ever more important. Blocking [9] ortiling [18] is a wellknown technique that improves the data locality of numerical algorithms [1, 6, 7, 12, 13].Tiling c ..."
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Cited by 805 (16 self)
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1 Introduction As processor speed continues to increase faster than memory speed, optimizations to use the memory hierarchy efficiently become ever more important. Blocking [9] ortiling [18] is a wellknown technique that improves the data locality of numerical algorithms [1, 6, 7, 12, 13].Tiling
Results 1  10
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1,962,969