Topic
Multiple kernel learning
About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.
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Papers
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TL;DR: This work compares multiple kernel learning and the proposed regularized variant in terms of accuracy, support vector count, and the number of kernels selected and sees that the proposed variant achieves statistically similar or higher accuracy results by using fewer kernel functions and/or support vectors through suitable regularization.
15 citations
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TL;DR: This work generalizes the framework of Multiple Kernel Learning (Mkl) for this cost-conscious methodology and sees that integrating the cost factor into kernel combination allows us to obtain cheaper kernel combinations by using fewer active kernels and/or support vectors.
15 citations
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TL;DR: In the proposed work, a recently introduced kernel learning technique called Multiple Kernel Learning (MKL) is used to optimally weight the kernel matrices of the sub-SVM classifiers to combine decision results of the SVM-based ensemble technique.
Abstract: Recently, a SVM-based ensemble learning technique has been introduced by the authors for hyperspectral plume
detection/classification. The SVM-based ensemble learning consists of a number of SVM classifiers and the
decisions from these sub-classifiers are combined to generate a final ensemble decision. The SVM-based ensemble
technique first randomly selects spectral feature subspaces from the input data. Each individual classifier
then independently conducts its own learning within its corresponding spectral feature space. Each classifier
constitutes a weak classifier. These weak classifiers are combined to make an ensemble decision. The ensemble
learning technique provides better performance than the conventional single SVM in terms of error rate. Various
aggregating techniques like bagging, boosting, majority voting and weighted averaging were used to combine
the weak classifiers, of which majority voting was found to be most robust. Yet, the ensemble of SVMs is suboptimal.
Techniques that optimally weight the individual decisions from the sub-classifiers are strongly desirable
to improve ensemble learning performance. In the proposed work, a recently introduced kernel learning technique
called Multiple Kernel Learning (MKL) is used to optimally weight the kernel matrices of the sub-SVM classifiers.
MKL basically iteratively performs l2 optimization on the Euclidian norm of the normal vector of the separating
hyperplane between the classes (background and chemical plume) defined by the weighted kernel matrix followed
by gradient descent optimization on the l1 regularized weighting coefficients of the individual kernel matrices.
Due to l1 regularization on the weighting coefficients, the optimized weighting coefficients become sparse. The
proposed work utilizes the sparse weighting coefficients to combine decision results of the SVM-based ensemble
technique. A performance comparison between the aggregating techniques - MKL and majority voting as applied
to hyperspectral chemical plume detection is presented in the paper.
15 citations
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TL;DR: An adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed that can detect DDoS attacks early and accurately.
Abstract: Distributed denial of service (DDoS) attacks have caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the inter-class mean with a gradient ascent and reducing the intra-class variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple kernel learning (SMKL) models with two characteristics including inter-class mean squared difference growth (M-SMKL) and intra-class variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.
15 citations
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TL;DR: An iterative framework is proposed which alternates between optimizing nearest neighbor classification for known categories with multiple kernel metric learning, and detecting clusters of unlabeled image regions likely to belong to a novel, unknown categories.
Abstract: The goal of an object category discovery system is to annotate a pool of unlabeled image data, where the set of labels is initially unknown to the system, and must therefore be discovered over time by querying a human annotator. The annotated data is then used to train object detectors in a standard supervised learning setting, possibly in conjunction with category discovery itself. Category discovery systems can be evaluated in terms of both accuracy of the resulting object detectors, and the efficiency with which they discover categories and annotate the training data. To improve the accuracy and efficiency of category discovery, we propose an iterative framework which alternates between optimizing nearest neighbor classification for known categories with multiple kernel metric learning, and detecting clusters of unlabeled image regions likely to belong to a novel, unknown categories. Experimental results on the MSRC and PASCAL VOC2007 data sets show that the proposed method improves clustering for category discovery, and efficiently annotates image regions belonging to the discovered classes.
15 citations