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Geometric SVM: A fast and intuitive SVM algorithm
 In Proceedings of the ICPR
, 1994
"... We present a geometrically motivated algorithm for finding the Support Vectors of a given set of points. This algorithm is reminiscent of the DirectSVM algorithm, in the way it picks data points for inclusion in the Support Vector set, but it uses an optimization based approach to add them to the ..."
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Cited by 3 (2 self)
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We present a geometrically motivated algorithm for finding the Support Vectors of a given set of points. This algorithm is reminiscent of the DirectSVM algorithm, in the way it picks data points for inclusion in the Support Vector set, but it uses an optimization based approach to add them
Geometric SVM: A Fast and Intuitive SVM Algorithm
"... We present a geometrically morivared algorithm forfinding rhe Suppon Vecrors of a given ser of poinrs. This algorirhm is reminiscenr of rhe DirecrSVM algorirlim, in rhe way ir picks dara poinrs for inclusion in the Supporr Vecror ser, bur ir uses an oprimizorion based approach ro add rhem 10 rhe ..."
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We present a geometrically morivared algorithm forfinding rhe Suppon Vecrors of a given ser of poinrs. This algorirhm is reminiscenr of rhe DirecrSVM algorirlim, in rhe way ir picks dara poinrs for inclusion in the Supporr Vecror ser, bur ir uses an oprimizorion based approach ro add rhem 10 rhe
Geometric Intuition and Algorithms for Eν–SVM
"... In this work we address the Eν–SVM model proposed by Pérez–Cruz et al. as an extension of the traditional ν support vector classification model (ν–SVM). Through an enhancement of the range of admissible values for the regularization parameter ν, the Eν–SVM has been shown to be able to produce a wid ..."
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wider variety of decision functions, giving rise to a better adaptability to the data. However, while a clear and intuitive geometric interpretation can be given for the ν–SVM model as a nearest–point problem in reduced convex hulls (RCH–NPP), no previous work has been made in developing such intuition
Svmknn: Discriminative nearest neighbor classification for visual category recognition
 in CVPR
, 2006
"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."
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Cited by 333 (10 self)
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We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While
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
SimpleSVM
"... We present a fast iterative support vector training algorithm for a large variety of different formulations. It works by incrementally changing a candidate support vector set using a greedy approach, until the supporting hyperplane is found within a finite number of iterations. It is derived from a ..."
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Cited by 3 (0 self)
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We present a fast iterative support vector training algorithm for a large variety of different formulations. It works by incrementally changing a candidate support vector set using a greedy approach, until the supporting hyperplane is found within a finite number of iterations. It is derived from a
FAST VOLUME RENDERING USING A SHEARWARP FACTORIZATION OF THE VIEWING TRANSFORMATION
, 1995
"... Volume rendering is a technique for visualizing 3D arrays of sampled data. It has applications in areas such as medical imaging and scientific visualization, but its use has been limited by its high computational expense. Early implementations of volume rendering used bruteforce techniques that req ..."
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Cited by 541 (2 self)
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that require on the order of 100 seconds to render typical data sets on a workstation. Algorithms with optimizations that exploit coherence in the data have reduced rendering times to the range of ten seconds but are still not fast enough for interactive visualization applications. In this thesis we present a
Duality and Geometry in SVM Classifiers
 In Proc. 17th International Conf. on Machine Learning
, 2000
"... We develop an intuitive geometric interpretation of the standard support vector machine (SVM) for classification of both linearly separable and inseparable data and provide a rigorous derivation of the concepts behind the geometry. For the separable case finding the maximum margin between the ..."
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Cited by 80 (4 self)
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We develop an intuitive geometric interpretation of the standard support vector machine (SVM) for classification of both linearly separable and inseparable data and provide a rigorous derivation of the concepts behind the geometry. For the separable case finding the maximum margin between
Inductive Learning Algorithms and Representations for Text Categorization
, 1998
"... Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categori ..."
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Cited by 641 (8 self)
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Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text
Instancebased learning algorithms
 Machine Learning
, 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
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Cited by 1359 (18 self)
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to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instancebased learning, that generates classification predictions using only specific instances. Instancebased learning algorithms do not maintain a set of abstractions derived from specific instances
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