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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 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
References for Data Stream Algorithms
, 2007
"... Many scenarios, such as network analysis, utility monitoring, and financial applications, generate massive streams of data. These streams consist of millions or billions of simple updates every hour, and must be processed to extract the information described in tiny pieces. These notes provide an in ..."
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an introduction to (and set of references for) data stream algorithms, and some of the techniques that have been developed over recent years to help mine the data while avoiding drowning in these massive flows of information. 1
Data Streaming Algorithms for Geometric Problems
"... A data stream is an ordered sequence of points that can be read only once or a small number of times. Formally, a data stream is a sequence of points ..."
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A data stream is an ordered sequence of points that can be read only once or a small number of times. Formally, a data stream is a sequence of points
Data streaming algorithms for estimating entropy of network traffic
 In Proceedings of the joint international conference on Measurement and modeling of computer systems (SIGMETRICS). ACM
, 2006
"... • Given n flows of sizes a1,..., an. Let s ≡ i ai. The empirical entropy is defined as ..."
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Cited by 72 (12 self)
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• Given n flows of sizes a1,..., an. Let s ≡ i ai. The empirical entropy is defined as
A SpaceOptimal DataStream Algorithm for Coresets in the Plane
"... Given a point set P ⊆ R², a subset Q ⊆ P is an εkernel of P if for every slab W containing Q, the (1+ε)expansion of W also contains P. We present a datastream algorithm for maintaining an εkernel of a stream of points in R² that uses O(1/√ε) space and takes O(log(1/ε)) amortized time to process ..."
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Cited by 20 (6 self)
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Given a point set P ⊆ R², a subset Q ⊆ P is an εkernel of P if for every slab W containing Q, the (1+ε)expansion of W also contains P. We present a datastream algorithm for maintaining an εkernel of a stream of points in R² that uses O(1/√ε) space and takes O(log(1/ε)) amortized time to process
A SpaceOptimal DataStream Algorithm for Coresets in the Plane ∗
"... Given a point set P ⊆ R 2, a subset Q ⊆ P is an εkernel of P if for every slab W containing Q, the (1 + ε)expansion of W also contains P. We present a datastream algorithm for maintaining an εkernel of a stream of points in R 2 that uses O(1 / √ ε) space and takes O(log(1/ε)) amortized time to ..."
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Given a point set P ⊆ R 2, a subset Q ⊆ P is an εkernel of P if for every slab W containing Q, the (1 + ε)expansion of W also contains P. We present a datastream algorithm for maintaining an εkernel of a stream of points in R 2 that uses O(1 / √ ε) space and takes O(log(1/ε)) amortized time
Results 1  10
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2,245,570