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Minkowski Sum Selection and Finding
, 2008
"... Let P, Q ⊆ R2 be two npoint multisets and Ar ≥ b be a set of λ inequalities on x and y, where A ∈ Rλ×2, r = [ x y], and b ∈ Rλ. Define the constrained Minkowski sum (P ⊕Q)Ar≥b as the multiset ..."
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Let P, Q ⊆ R2 be two npoint multisets and Ar ≥ b be a set of λ inequalities on x and y, where A ∈ Rλ×2, r = [ x y], and b ∈ Rλ. Define the constrained Minkowski sum (P ⊕Q)Ar≥b as the multiset
The 25th Workshop on Combinatorial Mathematics and Computation Theory Constrained Minkowski Sum Selection and Finding
"... Let P, Q ⊆ R2 be two npoint multisets and Ar ≥ b be a set of λ inequalities on x and y, where A ∈ Rλ×2, r = [ x y], and b ∈ Rλ. Define the constrained Minkowski sum (P ⊕ Q)Ar≥b as the multiset {(p + q)p ∈ P, q ∈ Q, A(p + q) ≥ b}. Given P, Q, Ar ≥ b, an objective function f: R2 → R, and a positive ..."
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positive integer k, the Minkowski Sum Selection Problem is to find the kth largest objective value among all objective values of points in (P ⊕ Q)Ar≥b. Given P, Q, Ar ≥ b, an objective function f: R2 → R, and a real number δ, the Minkowski Sum Finding Problem is to find a point (x
Minkowski
"... sum based lattice construction for solving simultaneous modular equations and applications to RSA ..."
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sum based lattice construction for solving simultaneous modular equations and applications to RSA
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sumproduct, cluster variational methods, expectationpropagation, mean field methods, maxproduct and linear programming relaxation, as well as conic programming relaxations — can
VisualSEEk: a fully automated contentbased image query system
, 1996
"... 1 We describe a highly functional prototype system for searching by visual features in an image database. The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions. The system finds the images that contain the most similar arrangements of ..."
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Cited by 752 (31 self)
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1 We describe a highly functional prototype system for searching by visual features in an image database. The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions. The system finds the images that contain the most similar arrangements
Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1051 (12 self)
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is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut
Attention, similarity, and the identificationCategorization Relationship
, 1986
"... A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification dat ..."
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Cited by 663 (28 self)
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, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained
Contentbased image retrieval at the end of the early years
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... The paper presents a review of 200 references in contentbased image retrieval. The paper starts with discussing the working conditions of contentbased retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for imag ..."
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Cited by 1594 (24 self)
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The paper presents a review of 200 references in contentbased image retrieval. The paper starts with discussing the working conditions of contentbased retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensorbased planning, visibility, decisiontheoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.
Results 1  10
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