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On the Expressiveness of SubsetSum Representations
 Acta Inform
, 2000
"... We develop a general theory for representing information as sums of elements in a subset of the basic set A of numbers of cardinality n, often refered to as a "knapsack vector". How many numbers can be represented in this way depends heavily on A. The lower, resp. upper, bound for the c ..."
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for expressiveness. Keywords: subsetsum, knapsack vector, expressiveness, injectivity Introduction Consider a finite set A of positive integers. Actually we need only the assumption that a commutative and an associative operation + is defined on A. Then each subset SA of A represents a number, namely, the sum
Quantum algorithms for the subsetsum problem
"... Abstract. This paper introduces a subsetsum algorithm with heuristic asymptotic cost exponent below 0.25. The new algorithm combines the 2010 HowgraveGraham–Joux subsetsum algorithm with a new streamlined data structure for quantum walks on Johnson graphs. ..."
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Abstract. This paper introduces a subsetsum algorithm with heuristic asymptotic cost exponent below 0.25. The new algorithm combines the 2010 HowgraveGraham–Joux subsetsum algorithm with a new streamlined data structure for quantum walks on Johnson graphs.
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a MixtureofGaussians model (for multimodal distributions). These probability densities are then used to formulate a maximumlikelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects such as hands.
Inductive Learning Algorithms and Representations for Text Categorization
, 1998
"... Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categori ..."
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Cited by 641 (8 self)
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categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs
A Fast Approximation Algorithm for the SubsetSum Problem
, 1999
"... The subsetsum problem (SSP) is defined as follows: given a positive integer bound and a set of n positive integers find a subset whose sum is closest to, but not greater than, the bound. We present a randomized approximation algorithm for this problem with linear space complexity and time complexit ..."
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Cited by 8 (0 self)
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The subsetsum problem (SSP) is defined as follows: given a positive integer bound and a set of n positive integers find a subset whose sum is closest to, but not greater than, the bound. We present a randomized approximation algorithm for this problem with linear space complexity and time
KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
, 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
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Cited by 930 (41 self)
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In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many
Subset–sum phase transitions and data compression
"... Abstract. We propose a rigorous analysis approach for the subset sum problem in the context of lossless data compression, where the phase transition of the subset sum problem is directly related to the passage between ambiguous and non–ambiguous decompression, for a compression scheme that is based ..."
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Abstract. We propose a rigorous analysis approach for the subset sum problem in the context of lossless data compression, where the phase transition of the subset sum problem is directly related to the passage between ambiguous and non–ambiguous decompression, for a compression scheme that is based
The DLV System for Knowledge Representation and Reasoning
 ACM Transactions on Computational Logic
, 2002
"... Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely believ ..."
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Cited by 455 (100 self)
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Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely
Results 1  10
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