Name
Abstract | ||
|---|---|---|
The Support Vector Machine (SVM) is an effective classification tool. Though extremely effective, SVMs are not a panacea. SVM training and testing is computationally expensive. Also, tuning the kernel parameters is a complicated procedure. On the other hand, the Nearest Neighbor (KNN) classifier is computationally efficient. In order to achieve the classification efficiency of an SVM and the computational efficiency of a KNN classifier, it has been shown previously that, rather than training a single global SVM, a separate SVM can be trained for the neighbourhood of each query point. In this work, we have extended this Local SVM (LSVM) formulation. Our Local Adaptive SVM (LASVM) formulation trains a local SVM in a modified neighborhood space of a query point. The main contributions of the paper are twofold: First, we present a novel LASVM algorithm to train a local SVM. Second, we discuss in detail the motivations behind the LSVM and LASVM formulations and its possible impacts on tuning the kernel parameters of an SVM. We found that training an SVM in a local adaptive neighborhood can result in significant classification performance gain. Experiments have been conducted on a selection of the UCIML, face, object, and digit databases. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/DICTA.2010.44 | DICTA |
Keywords | Field | DocType |
local svm,single global svm,separate svm,kernel parameter,query point,local adaptive svm,object recognition,classification efficiency,svm training,effective classification tool,lasvm formulation,measurement,face,kernel,support vector machine,nearest neighbor,support vector machines,databases | k-nearest neighbors algorithm,Kernel (linear algebra),Ranking SVM,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Classifier (linguistics),Machine learning,Cognitive neuroscience of visual object recognition,Nearest neighbor classifier | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nayyar Abbas Zaidi | 1 | 91 | 9.88 |
| David McG. Squire | 2 | 548 | 50.32 |