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Efficient Variants of the ICP Algorithm

by Szymon Rusinkiewicz, Marc Levoy - INTERNATIONAL CONFERENCE ON 3-D DIGITAL IMAGING AND MODELING , 2001
"... The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minim ..."
Abstract - Cited by 718 (5 self) - Add to MetaCart
The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points

On Sequential Monte Carlo Sampling Methods for Bayesian Filtering

by Arnaud Doucet, Simon Godsill, Christophe Andrieu - STATISTICS AND COMPUTING , 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is develop ..."
Abstract - Cited by 1051 (76 self) - Add to MetaCart
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework

CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

by D. Needell, J. A. Tropp - California Institute of Technology, Pasadena , 2008
"... Abstract. Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery alg ..."
Abstract - Cited by 770 (13 self) - Add to MetaCart
Abstract. Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery

CURE: An Efficient Clustering Algorithm for Large Data sets

by Sudipto Guha, Rajeev Rastogi, Kyuseok Shim - Published in the Proceedings of the ACM SIGMOD Conference , 1998
"... Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new clustering ..."
Abstract - Cited by 722 (5 self) - Add to MetaCart
is much better than those found by existing algorithms. Furthermore, they demonstrate that random sampling and partitioning enable CURE to not only outperform existing algorithms but also to scale well for large databases without sacrificing clustering quality. 1

A Sequential Algorithm for Training Text Classifiers

by David D. Lewis, William A. Gale , 1994
"... The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was ..."
Abstract - Cited by 631 (10 self) - Add to MetaCart
The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers

Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization

by Carlos M. Fonseca, Peter J. Fleming , 1993
"... The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
Abstract - Cited by 633 (15 self) - Add to MetaCart
satisfactory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-off surface.

Dynamic programming algorithm optimization for spoken word recognition

by Hiroaki Sakoe, Seibi Chiba - IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING , 1978
"... This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using timewarping function. Then, two time-normalized distance definitions, ded symmetric and asymmetric forms, are der ..."
Abstract - Cited by 788 (3 self) - Add to MetaCart
This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using timewarping function. Then, two time-normalized distance definitions, ded symmetric and asymmetric forms

Sampling Large Databases for Association Rules

by Hannu Toivonen , 1996
"... Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very signi cant for very large databases. We present new algorithms that reduce the data ..."
Abstract - Cited by 470 (3 self) - Add to MetaCart
the database activity considerably. Theidea is to pick a random sample, to ndusingthis sample all association rules that probably hold in the whole database, and then to verify the results with the restofthe database. The algorithms thus produce exact association rules, not approximations based on a sample

Understanding and using the Implicit Association Test: I. An improved scoring algorithm

by Anthony G. Greenwald, T. Andrew Poehlman, Eric Luis Uhlmann, Mahzarin R. Banaji, Anthony G. Greenwald - Journal of Personality and Social Psychology , 2003
"... behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures by Implic ..."
Abstract - Cited by 632 (94 self) - Add to MetaCart
behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures

Selective sampling using the Query by Committee algorithm

by Yoav Freund, H. Sebastian Seung, Eli Shamir, Naftali Tishby - Machine Learning , 1997
"... We analyze the "query by committee" algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially with the numbe ..."
Abstract - Cited by 433 (7 self) - Add to MetaCart
We analyze the "query by committee" algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially
Results 1 - 10 of 28,524