Open-Box Training of Kernel Support Vector Machines: Opportunities and Limitations (Unknown language)

In: Vision, Modeling and Visualization   ;  63-72  ;  2019

How to get this document?

Commercial Copyright fee: €14.50 Basic fee: €4.00 Total price: €18.50
Academic Copyright fee: €4.50 Basic fee: €2.00 Total price: €6.50

Kernel Support Vector Machines (SVMs) are widely used for supervised classification, and have achieved state-of-the-art performance in numerous applications. We aim to further increase their efficacy by allowing a human operator to steer their training process. To this end, we identify several possible strategies for meaningful human intervention in their training, propose a corresponding visual analytics workflow, and implement it in a prototype system. Initial results from two users, on data from three different domains suggest that, in addition to facilitating better insight into the data and into the classifier's decision process, visual analytics can increase the efficacy of Support Vector Machines when the data available for training has a low number of samples, is unbalanced with respect to the different classes, contains outliers, irrelevant features, or mislabeled samples. However, we also discuss some limitations of improving the efficacy of supervised classification with visual analytics.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

RodMesh: Two-handed 3D Surface Modeling in Virtual Reality
Verhoeven, Floor / Sorkine-Hornung, Olga | 2019
Reflection Symmetry in Textured Sewing Patterns
Wolff, Katja / Herholz, Philipp / Sorkine-Hornung, Olga | 2019
Normal Map Bias Reduction for Many-Lights Multi-View Photometric Stereo
Gan, Jiangbin / Bergen, Philipp / Thormählen, Thorsten / Drescher, Philip / Hagens, Ralf | 2019
Trigonometric Moments for Editable Structured Light Range Finding
Werner, Sebastian / Iseringhausen, Julian / Callenberg, Clara / Hullin, Matthias | 2019
Reconfigurable Snapshot HDR Imaging Using Coded Masks and Inception Network
Alghamdi, Masheal / Fu, Qiang / Thabet, Ali / Heidrich, Wolfgang | 2019
Stochastic Convolutional Sparse Coding
Xiong, Jinhui / Richtarik, Peter / Heidrich, Wolfgang | 2019
Learning a Perceptual Quality Metric for Correlation in Scatterplots
Wöhler, Leslie / Zou, Yuxin / Mühlhausen, Moritz / Albuquerque, Georgia / Magnor, Marcus | 2019
Open-Box Training of Kernel Support Vector Machines: Opportunities and Limitations
Khatami, Mohammad / Schultz, Thomas | 2019
Cluster-based Analysis of Multi-Parameter Distributions in Cloud Simulation Ensembles
Kumpf, Alexander / Stumpfegger, Josef / Westermann, Rüdiger | 2019
Clustering Ensembles of 3D Jet-Stream Core Lines
Kern, Michael / Westermann, Rüdiger | 2019
Visual Analytics of Simulation Ensembles for Network Dynamics
Ngo, Quynh Quang / Hütt, Marc-Thorsten / Linsen, Lars | 2019
Multi-Level-Memory Structures for Adaptive SPH Simulations
Winchenbach, Rene / Kolb, Andreas | 2019
Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs
Mueller-Roemer, Johannes Sebastian / Stork, André / Fellner, Dieter W. | 2019
Polarization Demosaicking for Monochrome and Color Polarization Focal Plane Arrays
Qiu, Simeng / Fu, Qiang / Wang, Congli / Heidrich, Wolfgang | 2019
Consistent Filtering of Videos and Dense Light-Fields Without Optic-Flow
Shekhar, Sumit / Semmo, Amir / Trapp, Matthias / Tursun, Okan Tarhan / Pasewaldt, Sebastian / Myszkowski, Karol / Döllner, Jürgen | 2019
Local Remote Photoplethysmography Signal Analysis for Application in Presentation Attack Detection
Kossack, Benjamin / Wisotzky, Eric L. / Hilsmann, Anna / Eisert, Peter | 2019
Visual Analysis of Probabilistic Infection Contagion in Hospitals
Wunderlich, Marcel / Block, Isabelle / Landesberger, Tatiana von / Petzold, Markus / Marschollek, Michael / Scheithauer, Simone | 2019
A Visual Analytics Tool for Cohorts in Motion Data
Sheharyar, Ali / Ruh, Alexander / Valkov, Dimitar / Markl, Michael / Bouhali, Othmane / Linsen, Lars | 2019
Visualizing Transport and Mixing in Particle-based Fluid Flows
Rapp, Tobias / Dachsbacher, Carsten | 2019