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6,647
Robust Classification for Imprecise Environments
, 1989
"... In realworld environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We present a method for the comparison of classifier performance that is robust to imprecise class distributions and misclas ..."
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Cited by 341 (15 self)
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and misclassification costs. The ROC convex hull method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
, 2010
"... Stochastic subgradient methods are widely used, well analyzed, and constitute effective tools for optimization and online learning. Stochastic gradient methods ’ popularity and appeal are largely due to their simplicity, as they largely follow predetermined procedural schemes. However, most common s ..."
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Cited by 311 (3 self)
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subgradient approaches are oblivious to the characteristics of the data being observed. We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradientbased learning. The adaptation
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In:
 3rd International Conference on Knowledge Discovery and Data Mining,
, 1997
"... Abstract Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust t ..."
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Cited by 313 (15 self)
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Abstract Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust
Background independent quantum gravity: a status report
, 2004
"... The goal of this article is to present an introduction to loop quantum gravity —a background independent, nonperturbative approach to the problem of unification of general relativity and quantum physics, based on a quantum theory of geometry. Our presentation is pedagogical. Thus, in addition to pr ..."
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Cited by 259 (7 self)
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The goal of this article is to present an introduction to loop quantum gravity —a background independent, nonperturbative approach to the problem of unification of general relativity and quantum physics, based on a quantum theory of geometry. Our presentation is pedagogical. Thus, in addition
Black hole entropy from nearhorizon microstates,
 JHEP 9802,
, 1998
"... Abstract: Black holes whose nearhorizon geometries are locally, but not necessarily globally, AdS 3 (threedimensional antide Sitter space) are considered. Using the fact that quantum gravity on AdS 3 is a conformal field theory, we microscopically compute the black hole entropy from the asymptot ..."
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Cited by 284 (6 self)
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Abstract: Black holes whose nearhorizon geometries are locally, but not necessarily globally, AdS 3 (threedimensional antide Sitter space) are considered. Using the fact that quantum gravity on AdS 3 is a conformal field theory, we microscopically compute the black hole entropy from
Discreteness of area and volume in quantum gravity
, 2008
"... We study the operator that corresponds to the measurement of volume, in nonperturbative quantum gravity, and we compute its spectrum. The operator is constructed in the loop representation, via a regularization procedure; it is finite, background independent, and diffeomorphisminvariant, and there ..."
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Cited by 242 (35 self)
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of the volume diagonalize also the area operator. We argue that the spectra of volume and area determined here can be considered as predictions of the looprepresentation formulation of quantum gravity on the outcomes of (hypothetical) Planckscale sensitive measurements of the geometry of space.
Incremental and Decremental Support Vector Machine Learning
, 2000
"... An online recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the KuhnTucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is reversible, and decrement ..."
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Cited by 251 (4 self)
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, and decremental "unlearning" offers an efficient method to exactly evaluate leaveoneout generalization performance. Interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of the data. 1
Orientifolds and Mirror symmetry
, 2003
"... We study parity symmetries and crosscap states in classes of N = 2 supersymmetric quantum field theories in 1+1 dimensions, including nonlinear sigma models, gauged WZW models, LandauGinzburg models, and linear sigma models. The parity anomaly and its cancellation play important roles in many of t ..."
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Cited by 271 (11 self)
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We study parity symmetries and crosscap states in classes of N = 2 supersymmetric quantum field theories in 1+1 dimensions, including nonlinear sigma models, gauged WZW models, LandauGinzburg models, and linear sigma models. The parity anomaly and its cancellation play important roles in many
The partigame algorithm for variable resolution reinforcement learning in multidimensional statespaces
 MACHINE LEARNING
, 1995
"... Partigame is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous statespaces. In high dimensions it is essential that learning does not plan uniformly over a statespace. Partigame maintains a decisiontree partitioning of statespace and applies tec ..."
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Cited by 255 (9 self)
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Partigame is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous statespaces. In high dimensions it is essential that learning does not plan uniformly over a statespace. Partigame maintains a decisiontree partitioning of statespace and applies
Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
 SIAM JOURNAL ON SCIENTIFIC COMPUTING
, 2004
"... Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized ..."
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Cited by 261 (15 self)
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data points sampled with noise from the manifold, we represent the local geometry of the manifold using tangent spaces learned by fitting an affine subspace in a neighborhood of each data point. Those tangent spaces are aligned to give the internal global coordinates of the data points with respect
Results 11  20
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6,647