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3,059
Strictly Proper Scoring Rules, Prediction, and Estimation
, 2007
"... Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if he ..."
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Cited by 373 (28 self)
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and to crossvalidation, and propose a novel form of crossvalidation known as randomfold crossvalidation. A case study on probabilistic weather forecasts in the North American Pacific Northwest illustrates the importance of propriety. We note optimum score approaches to point and quantile
A Note on Platt's Probabilistic Outputs for Support Vector Machines
, 2003
"... Platt's probabilistic outputs for Support Vector Machines [6] has been popular for applications that require posterior class probabilities. In this note, we propose an improvement which theoretically converges and avoids numerical difficulties. A simpler and readytouse pseudo code is includ ..."
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Cited by 190 (5 self)
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Platt's probabilistic outputs for Support Vector Machines [6] has been popular for applications that require posterior class probabilities. In this note, we propose an improvement which theoretically converges and avoids numerical difficulties. A simpler and readytouse pseudo code
Stress, coping and social support processes: Where are we? What next?
 Journal of Health and Social Behavior,
, 1995
"... JSTOR is a notforprofit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about J ..."
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Cited by 259 (1 self)
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of physical and mental health outcomes, the "carryovers" of stress from one role domain or stage of life into another, the benefits derived from negative experiences, and the determinants of the meaning of stressors. Although a sense of personal control and perceived social support influence health
The effectiveness of lloydtype methods for the kmeans problem
 In FOCS
, 2006
"... We investigate variants of Lloyd’s heuristic for clustering high dimensional data in an attempt to explain its popularity (a half century after its introduction) among practitioners, and in order to suggest improvements in its application. We propose and justify a clusterability criterion for data s ..."
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Cited by 84 (3 self)
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on clustering quality. Our main algorithmic contribution is a novel probabilistic seeding process for the starting configuration of a Lloydtype iteration. 1
Noisy kmeans The algorithm of noisy kmeans
, 2013
"... Editor: In this note, we introduce a new algorithm to deal with finite dimensional clustering with errors in variables. The design of this algorithm is based on recent theoretical advances (see Loustau (2013a,b)) in statistical learning with errors in variables. As the previous mentioned papers, the ..."
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, the algorithm mixes different tools from the inverse problem literature and the machine learning community. Coarsely, it is based on a twostep procedure: (1) a deconvolution step to deal with noisy inputs and (2) Newton’s iterations as the popular kmeans. Keywords: kmeans.
Videobased face recognition using probabilistic appearance manifolds
 In Proc. IEEE Conference on Computer Vision and Pattern Recognition
, 2003
"... This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a lowdimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), ..."
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Cited by 176 (5 self)
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), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a Kmeans algorithm; each cluster is represented as a plane computed through principal component analysis (PCA
10 kMeans Clustering
"... Probably the most famous clustering formulation is kmeans. This is the focus today. Note: kmeans is not an algorithm, it is a problem formulation. kMeans is in the family of assignment based clustering. Each cluster is represented by a single point, to which all other points in the cluster are “a ..."
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Probably the most famous clustering formulation is kmeans. This is the focus today. Note: kmeans is not an algorithm, it is a problem formulation. kMeans is in the family of assignment based clustering. Each cluster is represented by a single point, to which all other points in the cluster
Charting a Manifold
 Advances in Neural Information Processing Systems 15
, 2003
"... this paper we use m i ( j ) N ( j ; i , s ), with the scale parameter s specifying the expected size of a neighborhood on the manifold in sample space. A reasonable choice is s = r/2, so that 2erf(2) > 99.5% of the density of m i ( j ) is contained in the area around y i where the manifold i ..."
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Cited by 206 (7 self)
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pathology noted in [8]. Equation (3) is easily adapted to give a reduced number of charts and/or charts centered on local centroids. 4 Connecting the charts We now build a connection for set of charts specified as an arbitrary nondegenerate GMM. A GMM gives a soft partitioning of the dataset
A probabilistic algorithm for kSAT and constraint satisfaction problems
 In Proceedings of the 40th Annual IEEE Symposium on Foundations of Computer Science, FOCS'99
, 1999
"... We present a simple probabilistic algorithm for solving kSAT, and more generally, for solving constraint satisfaction problems (CSP). The algorithm follows a simple localsearch paradigm (cf. [9]): randomly guess an initial assignment and then, guided by those clauses (constraints) that are not sati ..."
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Cited by 158 (4 self)
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We present a simple probabilistic algorithm for solving kSAT, and more generally, for solving constraint satisfaction problems (CSP). The algorithm follows a simple localsearch paradigm (cf. [9]): randomly guess an initial assignment and then, guided by those clauses (constraints
Fast and accurate kmeans for large datasets.
 In NIPS*24,
, 2011
"... Abstract Clustering is a popular problem with many applications. We consider the kmeans problem in the situation where the data is too large to be stored in main memory and must be accessed sequentially, such as from a disk, and where we must use as little memory as possible. Our algorithm is base ..."
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Cited by 23 (0 self)
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then incorporate approximate nearest neighbor search to compute kmeans in o(nk) (where n is the number of data points; note that computing the cost, given a solution, takes Θ(nk) time). We show that our algorithm compares favorably to existing algorithms both theoretically and experimentally, thus providing
Results 1  10
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3,059