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62,426
What good are positive emotions
- Review of General Psychology
, 1998
"... This article opens by noting that positive emotions do not fit existing models of emotions. Consequently, a new model is advanced to describe the form and function of a subset of positive emotions, including joy, interest, contentment, and love. This new model posits that these positive emotions ser ..."
Abstract
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Cited by 454 (15 self)
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;quot;What good are positive emotions?" have yet to be articulated. This is unfortunate. being, and physical health. Experiences of positive emotion are central to Why Have Positive Emotions human nature and contribute richly to the quality of people's lives (Diener & Larsen, Been Marginalized
Clustering by passing messages between data points
- Science
, 2007
"... Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such “exemplars ” can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initi ..."
Abstract
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Cited by 696 (8 self)
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if that initial choice is close to a good solution. We devised a method called “affinity propagation,” which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges
Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Crystallogr. sect
- A
, 1991
"... Map interpretation remains a critical step in solving the structure of a macromolecule. Errors introduced at this early stage may persist throughout crystallo-graphic refinement and result in an incorrect struc-ture. The normally quoted crystallographic residual is often a poor description for the q ..."
Abstract
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Cited by 1051 (9 self)
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for the quality of the model. Strategies and tools are described that help to alleviate this problem. These simplify the model-building process, quantify the goodness of fit of the model on a per-residue basis and locate possible errors in pep-tide and side-chain conformations.
Ant Colony System: A cooperative learning approach to the traveling salesman problem
- IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1997
"... This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSP’s. Ants cooperate using an indirect form of communication mediated by a pher ..."
Abstract
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Cited by 1029 (53 self)
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This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSP’s. Ants cooperate using an indirect form of communication mediated by a
The Ant System: Optimization by a colony of cooperating agents
- IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B
, 1996
"... An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation ..."
Abstract
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Cited by 1300 (46 self)
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computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed
Genetic Programming
, 1997
"... Introduction Genetic programming is a domain-independent problem-solving 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 ..."
Abstract
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Cited by 1056 (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
A training algorithm for optimal margin classifiers
- PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
Abstract
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Cited by 1865 (43 self)
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is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC
Histograms of Oriented Gradients for Human Detection
- In CVPR
, 2005
"... We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly out ..."
Abstract
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Cited by 3735 (9 self)
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outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks
A survey of trust and reputation systems for online service provision
, 2005
"... Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to collect information about potential service providers in order to select the most reliable and trustworthy provider of services and information and to avoid th ..."
Abstract
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Cited by 632 (15 self)
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the less trustworthy. A natural side effect is that it also provides an incentive for good behaviour and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative
Optimizing Search Engines using Clickthrough Data
, 2002
"... This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches ..."
Abstract
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Cited by 1314 (23 self)
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This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous
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