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Adaptive Regularization of Weight Vectors
 Advances in Neural Information Processing Systems 22
, 2009
"... We present AROW, a new online learning algorithm that combines several useful properties: large margin training, confidence weighting, and the capacity to handle nonseparable data. AROW performs adaptive regularization of the prediction function upon seeing each new instance, allowing it to perform ..."
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Cited by 69 (17 self)
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We present AROW, a new online learning algorithm that combines several useful properties: large margin training, confidence weighting, and the capacity to handle nonseparable data. AROW performs adaptive regularization of the prediction function upon seeing each new instance, allowing
Optimal Weight Vectors for Broadcast Channels
 in Proc. IEEE Asilomar Conf. on Signals, Systems & Computers
, 1996
"... In a multitransmitter broadcast system, the weight vector for each message signal can provide an additional degreeoffreedom for signal enhancement and interference suppression by taking advantage of the spatial diversity among the users. To date, the design of the optimal weight vectors which max ..."
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Cited by 2 (2 self)
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In a multitransmitter broadcast system, the weight vector for each message signal can provide an additional degreeoffreedom for signal enhancement and interference suppression by taking advantage of the spatial diversity among the users. To date, the design of the optimal weight vectors which
Selection Weighted Vector Directional Filters
"... In this paper, a class of Weighted Vector Directional Filters (WVDFs) based on the selection of the output sample from the multichannel input set is analyzed and optimized. The WVDF output minimizes the sum of weighted angular distances to other input samples from the filtering window. Dependent o ..."
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Cited by 12 (3 self)
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In this paper, a class of Weighted Vector Directional Filters (WVDFs) based on the selection of the output sample from the multichannel input set is analyzed and optimized. The WVDF output minimizes the sum of weighted angular distances to other input samples from the filtering window. Dependent
Weight Vectors Of The Basic ...Module And The LittlewoodRichardson Rule
, 1995
"... The basic representation of A 1 is studied. The weight vectors are represented in terms of Schur functions. A suitable base of any weight space is given. LittlewoodRichardson rule appears in the linear relations among weight vectors. ..."
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The basic representation of A 1 is studied. The weight vectors are represented in terms of Schur functions. A suitable base of any weight space is given. LittlewoodRichardson rule appears in the linear relations among weight vectors.
Opportunistic beamforming based on multiple weighting vectors
 IEEE Trans. Wireless Commun
, 2005
"... Abstract—In order to improve the throughput of the opportunistic beamforming, the authors generalize the opportunistic beamforming by using multiple random weighting vectors at each time slot. The base station chooses the best weighting vector and performs the opportunistic beamforming with this op ..."
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Cited by 16 (0 self)
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Abstract—In order to improve the throughput of the opportunistic beamforming, the authors generalize the opportunistic beamforming by using multiple random weighting vectors at each time slot. The base station chooses the best weighting vector and performs the opportunistic beamforming
Computing semantic relatedness using Wikipediabased explicit semantic analysis
 In Proceedings of the 20th International Joint Conference on Artificial Intelligence
, 2007
"... Computing semantic relatedness of natural language texts requires access to vast amounts of commonsense and domainspecific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a highdimensional space of concepts derived from Wikipedi ..."
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Cited by 561 (9 self)
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Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipediabased concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 540 (5 self)
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In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 781 (29 self)
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of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a preliminary theoretical analysis of the statistical performance of our
ASSESSMENT OF THE APPLICABILITY OF THE WEIGHT VECTOR THEORY FOR CORIOLIS FLOWMETERS
, 2009
"... Abstract − The weight vector theory for Coriolis flow meters has been the subject of research presented by Hemp and coworkers in various articles. The underlying theory may not be easily understood. This paper explains the application of the weight vector theory for Coriolis flowmeters. The theory ..."
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Abstract − The weight vector theory for Coriolis flow meters has been the subject of research presented by Hemp and coworkers in various articles. The underlying theory may not be easily understood. This paper explains the application of the weight vector theory for Coriolis flowmeters. The theory
Indexing by latent semantic analysis
 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
, 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higherorder structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
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Cited by 3775 (35 self)
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. The particular technique used is singularvalue decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries
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