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Transformations of surface normal vectors
 Tech. Rep. 22, Apple Computer
, 1990
"... Abstract: Given an affine 4x4 modeling transformation matrix, we derive the matrix that represents the transformation of a surface’s normal vectors. This is similar to the modeling matrix only when any scaling is isotropic. We further derive results for transformations of light direction vectors and ..."
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Cited by 3 (0 self)
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Abstract: Given an affine 4x4 modeling transformation matrix, we derive the matrix that represents the transformation of a surface’s normal vectors. This is similar to the modeling matrix only when any scaling is isotropic. We further derive results for transformations of light direction vectors
New Computation of Normal Vector and Curvature
"... Abstract: The local geometric properties such as curvatures and normal vectors play important roles in analyzing the local shape of objects. The result of the geometric operations such as mesh simplification and mesh smoothing is dependent on how to compute the normal vectors and the curvatures of ..."
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Cited by 1 (1 self)
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Abstract: The local geometric properties such as curvatures and normal vectors play important roles in analyzing the local shape of objects. The result of the geometric operations such as mesh simplification and mesh smoothing is dependent on how to compute the normal vectors and the curvatures
A tutorial on support vector machines for pattern recognition
 Data Mining and Knowledge Discovery
, 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and nonseparable data, working through a nontrivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
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Cited by 3319 (12 self)
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The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and nonseparable data, working through a nontrivial example in detail. We describe a mechanical analogy, and discuss when
An approximation for Normal Vectors of Deformable Models
"... Abstract. A physicallybased deformable model proposed by Terzopoulous et al. is governed by the Lagrange’s form, that establishes the relation between the dynamics of deformable models under the influence of applied forces. The net instantaneous potential energy of deformation is derived on the bas ..."
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on the basis of the geometric properties, namely the first and second fundamental forms. For simplicity, the normal vector at each sample point is approximated by the second derivative. In this paper we present another approximation for the normal vector which offers better visual simulation. Some comparisons
Gene selection for cancer classification using support vector machines
 Machine Learning
"... Abstract. DNA microarrays now permit scientists to screen thousands of genes simultaneously and determine whether those genes are active, hyperactive or silent in normal or cancerous tissue. Because these new microarray devices generate bewildering amounts of raw data, new analytical methods must ..."
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Cited by 1075 (25 self)
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available training examples from cancer and normal patients, we build a classifier suitable for genetic diagnosis, as well as drug discovery. Previous attempts to address this problem select genes with correlation techniques. We propose a new method of gene selection utilizing Support Vector Machine methods
Asymptotically Normal Vectors by
, 2007
"... Abstract We obtain the Edgeworth expansion for P(n 1/2 ( ˆ θ − θ) < x) and its derivatives, and the tilted Edgeworth (or saddlepoint or small sample) expansion for P ( ˆ θ < x) and its derivatives where ˆθ is any vector estimate having the standard cumulant expansions in powers of n −1. ..."
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Abstract We obtain the Edgeworth expansion for P(n 1/2 ( ˆ θ − θ) < x) and its derivatives, and the tilted Edgeworth (or saddlepoint or small sample) expansion for P ( ˆ θ < x) and its derivatives where ˆθ is any vector estimate having the standard cumulant expansions in powers of n −1.
Distance Vector Multicast Routing Protocol
 RFC 1075, BBN
, 1988
"... This RFC describes a distancevectorstyle routing protocol for routing multicast datagrams through an internet. It is derived from the Routing Information Protocol (RIP) [1], and implements multicasting as described in RFC1054. This is an experimental protocol, and its implementation is not recomm ..."
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Cited by 477 (3 self)
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This RFC describes a distancevectorstyle routing protocol for routing multicast datagrams through an internet. It is derived from the Routing Information Protocol (RIP) [1], and implements multicasting as described in RFC1054. This is an experimental protocol, and its implementation
A Simple Estimator of Cointegrating Vectors in Higher Order Cointegrated Systems
 ECONOMETRICA
, 1993
"... Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. T ..."
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Cited by 507 (3 self)
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Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions
Transductive Inference for Text Classification using Support Vector Machines
, 1999
"... This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimiz ..."
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Cited by 887 (4 self)
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This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try
Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 620 (1 self)
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
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