A sequential dual method for large scale multi-class linear svms

Keerthi, S. Sathiya; Sellamanickam, Sundararajan; Chang, Kai-Wei; Hsieh, Cho‐Jui; Lin, Chih-Jen

doi:10.1145/1401890.1401942

Cited by 108 publications

(62 citation statements)

References 18 publications

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“…6. To emphasize the validity and efficacy of the learned network, we also compare it with another method utilizing multi-class support vector machine (SVM) with the Crammer and Singer multi-class SVM [21] implementation of [22] in LIBLINEAR [23] library. The feature vectors for training SVM are extracted from the VGG-F network of [20], their dimensionality is reduced to 256, and PCA whitening is applied.…”

Section: Superclass Classificationmentioning

confidence: 99%

Generic and attribute-specific deep representations for maritime vessels

Solmaz

Gundogdu

Yücesoy

et al. 2017

IPSJ T Comput Vis Appl

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Fine-grained visual categorization has recently received great attention as the volumes of labeled datasets for classification of specific objects, such as cars, bird species, and air-crafts, have been increasing. The availability of large datasets led to significant performance improvements in several vision-based classification tasks. Visual classification of maritime vessels is another important task, assisting naval security and surveillance applications. We introduced, MARVEL, a large-scale image dataset for maritime vessels, consisting of 2 million user-uploaded images and their various attributes, including vessel identity, type, category, year built, length, and tonnage, collected from a community website. The images were categorized into vessel type classes and also into superclasses defined by combining semantically similar classes, following a semi-automatic clustering scheme. For the analysis of the presented dataset, extensive experiments have been performed, involving several potentially useful applications: vessel type classification, identity verification, retrieval, and identity recognition with and without prior vessel type knowledge. Furthermore, we attempted interesting problems of visual marine surveillance such as predicting and classifying maritime vessel attributes such as length, summer deadweight, draught, and gross tonnage by solely interpreting the visual content in the wild, where no additional cues such as scale, orientation, or location are provided. By utilizing generic and attribute-specific deep representations for maritime vessels, we obtained promising results for the aforementioned applications.

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Section: Superclass Classificationmentioning

confidence: 99%

Generic and attribute-specific deep representations for maritime vessels

Solmaz

Gundogdu

Yücesoy

et al. 2017

IPSJ T Comput Vis Appl

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“…Here we extend the sequential dual method (SDM) presented in [8] to derive an efficient sequential derivation of the LSSVM dual variables. This algorithm uses the gradient information to optimize the dual variables and the weight vector in a sequential manner.…”

Section: B Computation Of the Lssvm Parametersmentioning

confidence: 99%

“…We refer to this new method as linear subclass SVMs (LSSVMs). For the efficient implementation of LSSVMs we exploit the sequential dual method (SDM) described in [8]. Moreover, we introduce a new nongaussianity measure for subclass partitioning, and exploit an ECOC framework for combining the binary subclass classifiers.…”

mentioning

confidence: 99%

Linear Subclass Support Vector Machines

Gkalelis

Mezaris

Kompatsiaris

et al. 2012

IEEE Signal Process. Lett.

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Abstract-In this letter, linear subclass support vector machines (LSSVMs) are proposed that can efficiently learn a piecewise linear decision function for binary classification problems. This is achieved using a nongaussianity criterion to derive the subclass structure of the data, and a new formulation of the optimization problem that exploits the subclass information. LSSVMs provide low computation cost during training and evaluation, and offer competitive recognition performance in comparison to other popular SVM-based algorithms. Experimental results on various datasets confirm the advantages of LSSVMs.

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“…LiblieaR is an R interface to LIBLINEAR, a C/C++ library for large linear classification (Fan et al, 2008). LIBLINEAR not only has good theoretical properties, but also shows promising performance in practice (Fan et al, 2008;Hsieh et al, 2008;Keerthi et al, 2008;Lin et al, 2008). L2-regularized L2-loss support vector classification model (Fan et al, 2008) in LiblieaR was applied to construct the predictor.…”

Section: Predictor Construction and Evaluationmentioning

confidence: 99%

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

et al. 2011

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Single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), are responsible for most of human genetic diseases. Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases. In this work, a method of deleterious SAP prediction at system level was established. Unlike most existing methods, our method not only considers the sequence and structure information, but also the network information. The integration of network information can improve the performance of deleterious SAP prediction. To make our method available to the public, we developed SySAP (a System-level predictor of deleterious Single Amino acid Polymorphisms), an easy-to-use and high accurate web server. SySAP is freely available at http://www.biosino.org/ SySAP/and http://lifecenter.sgst.cn/SySAP/.

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A sequential dual method for large scale multi-class linear svms

Cited by 108 publications

References 18 publications

Generic and attribute-specific deep representations for maritime vessels

Generic and attribute-specific deep representations for maritime vessels

Linear Subclass Support Vector Machines

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

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