Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of <i>Cercospora</i> leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.

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Support Vector Machines Training Data Selection Using a Genetic Algorithm

Lecture Notes in Computer Science - Structural, Syntactic, and Statistical Pattern Recognition ◽

10.1007/978-3-642-34166-3_61 ◽

2012 ◽

pp. 557-565 ◽

Cited By ~ 29

Author(s):

Michal Kawulok ◽

Jakub Nalepa

Keyword(s):

Genetic Algorithm ◽

Support Vector Machines ◽

Training Data ◽

Data Selection ◽

Support Vector ◽

Vector Machines ◽

Training Data Selection

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Speeding up Support Vector Machines - Probabilistic versus Nearest Neighbour Methods for Condensing Training Data

Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods ◽

10.5220/0004927003640371 ◽

2014 ◽

Keyword(s):

Support Vector Machines ◽

Training Data ◽

Support Vector ◽

Nearest Neighbour ◽

Vector Machines

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APPLICATIONS OF SUPPORT VECTOR MACHINES AS A ROBUST TOOL IN HIGH THROUGHPUT VIRTUAL SCREENING

International Journal for Computational Biology ◽

10.34040/ijcb.1.1.2012.12 ◽

2012 ◽

Vol 1 (1) ◽

pp. 43 ◽

Cited By ~ 1

Author(s):

Renu Vyas ◽

S.S. Tambe ◽

B.D. Kulkarni

Keyword(s):

Support Vector Machines ◽

Virtual Screening ◽

High Throughput ◽

Support Vector ◽

Vector Machines ◽

High Throughput Virtual Screening

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LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-447-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 447-452

Author(s):

M. Zhou ◽

C. R. Li ◽

L. Ma ◽

H. C. Guan

Keyword(s):

Support Vector Machines ◽

Land Cover ◽

Prediction Model ◽

Image Data ◽

Ground Truth ◽

Land Cover Classification ◽

Training Data ◽

Support Vector ◽

Vector Machines ◽

Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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