Learning to Classify Text Using Support Vector Machines

Joachims, Thorsten

doi:10.1007/978-1-4615-0907-3

Cited by 1,178 publications

(882 citation statements)

References 4 publications

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“…γ is a scaling factor on the distance between the support vectors ζ m and the query points ζ . C determines a tradeoff between increasing accuracy and optimizing for the complexity of the fit [61]. A small γ , likewise a high C will fit well with local nonlinearities.…”

Section: Parameter Selectionmentioning

confidence: 99%

Estimating the non-linear dynamics of free-flying objects

Kim

Billard

2012

Robotics and Autonomous Systems

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a b s t r a c tThis paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass.To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing.

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Section: Parameter Selectionmentioning

confidence: 99%

Estimating the non-linear dynamics of free-flying objects

Kim

Billard

2012

Robotics and Autonomous Systems

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“…The steps explained above are all performed in Matlab by the SVM light package from Joachims [21]. The package returns a classification-model based on the given training set, which can then be used to classify a test set.…”

Section: Classificationmentioning

confidence: 99%

Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

Conradsen¹,

Beniczky²,

Wolf³

et al. 2012

Computer Methods and Programs in Biomedicine

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“…Here, we utilized the SVM light implementation of the support vector machine algorithm [34,35]. Additionally, the models learnt on the IKEA database are tested for their applicability on Swissranger frames acquired in two real flats.…”

Section: Evaluation Of Room Type Categorizationmentioning

confidence: 99%

Indoor Scene Classification Using Combined 3D and Gist Features

Swadzba

Wachsmuth

2011

Computer Vision – ACCV 2010

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Abstract. Scene categorization is an important mechanism for providing high-level context which can guide methods for a more detailed analysis of scenes. State-of-the-art techniques like Torralba's Gist features show a good performance on categorizing outdoor scenes but have problems in categorizing indoor scenes. In contrast to object based approaches, we propose a 3D feature vector capturing general properties of the spatial layout of indoor scenes like shape and size of extracted planar patches and their orientation to each other. This idea is supported by psychological experiments which give evidence for the special role of 3D geometry in categorizing indoor scenes. In order to study the influence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for defining 3D features on planar surfaces extracted in 3D data. Additionally, we propose a voting technique to fuse 3D features and 2D Gist features and show in our experiments a significant contribution of the 3D features to the indoor scene categorization task.

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Learning to Classify Text Using Support Vector Machines

Cited by 1,178 publications

References 4 publications

Estimating the non-linear dynamics of free-flying objects

Estimating the non-linear dynamics of free-flying objects

Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

Indoor Scene Classification Using Combined 3D and Gist Features

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