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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TL;DR: The improved SVM called the multiple kernel support vector machine was presented, and the mathematical formulation of multiple kernel learning is given and the implementation for average molecular weight in polyacrylonitrile productive process demonstrates the good performance of the proposed method compared to single kernel.
Abstract: Soft sensing technology is one of the topics of general interest in study on current process control, which has recently drawn considerable attention worldwide, and has stimulated researchers and engineers to make greater effort to reduce the cost/benefit-ratio for development and manufacture of bio-industrial processes both economically and environmentally. This paper introduced a kind of soft-sensor based on an improved support vector machine (SVM) for a polyacrylonitrile productive process. The improved SVM called the multiple kernel support vector machine was presented, and the mathematical formulation of multiple kernel learning is given. Through the implementation for average molecular weight in polyacrylonitrile productive process, it demonstrates the good performance of the proposed method compared to single kernel.
2 citations
01 Jan 2011
TL;DR: The idea of incorporating several data sources in analysis may be beneficial by reducing the noise, as well as by improving statistical significance and leveraging the interactions and correlations between data sources to obtain more refined and higher-level information, which is known as data fusion.
Abstract: In the era of information overflow, data mining and machine learning are indispensable tools to retrieve information and knowledge from data The idea of incorporating several data sources in analysis may be beneficial by reducing the noise, as well as by improving statistical significance and leveraging the interactions and correlations between data sources to obtain more refined and higher-level information [50], which is known as data fusion In bioinformatics, considerable effort has been devoted to genomic data fusion, which is an emerging topic pertaining to a lot of applications At present, terabytes of data are generated by high-throughput techniques at an increasing rate In data fusion, these terabytes are further multiplied by the number of data sources or the number of species A statistical model describing this data is therefore not an easy matter To tackle this challenge, it is rather effective to consider the data as being generated by a complex and unknown black box with the goal of finding a function or an algorithm that operates on an input to predict the output About 15 years ago, Boser [8] and Vapnik [51] introduced the support vector method which makes use of kernel functions This method has offered plenty of opportunities to solve complicated problems but also brought lots of interdisciplinary challenges in statistics, optimization theory, and the applications therein [40]
2 citations
15 Sep 2015
TL;DR: This approach, referred to as MKL clm, enriches the feature set of the multiple kernel learning (MKL) framework through the incorporation of classemes, which represent the outputs of multiple class-specific classifiers trained from the data of each informatics domain.
Abstract: We present a comprehensive and fully automatic glaucoma detection approach that uses machine learning techniques over multiple informatics do- mains, consisting of personal profile data, genetic data, and retinal image data. This approach, referred to as MKL clm , enriches the feature set of the multiple kernel learning (MKL) framework through the incorporation of classemes, which represent the outputs of multiple class-specific classifiers trained from the data of each informatics domain. We validate our MKL clm framework on a population- based dataset consisting of 2258 subjects, achieving an AUC of 94.9% ± 1.7% and a specificity of 88.5%±2.7% at 85% sensitivity, which is significantly better than the current clinical standard of care which uses intraocular pressure (IOP) for glaucoma detection. The experiments also demonstrate that MKL clm outper- forms the standard SVM method using data from individual domains, as well as the traditional MKL method, showing that this deeper integration of data from different informatics domains can lead to significant gains in holistic glaucoma diagnosis and screening.
2 citations
01 Jul 2016
TL;DR: An ensemble learning framework, SMKB (Stochastic Multiple Kernel Boosting), which applies Adaptive Boosting (AdaBoost), stochastic approach to learning multiple kernel-based classifier for multi-class classification problem is presented and experimental results show that SMKB is more effective, efficient than traditional MKL techniques.
Abstract: Hyperspectral image classification has been a very active area of research in recent years. Multiple kernel learning (MKL), ensemble learning are promising family of machine learning algorithms, have been applied extensively in hyperspectral image classification. However, many MKL methods often formulate the problem as an optimization task. Due to the high computational cost of solving the complicated optimization problem, improve the efficiency of MKL, in this paper, an ensemble learning framework, SMKB (Stochastic Multiple Kernel Boosting), which applies Adaptive Boosting (AdaBoost), stochastic approach to learning multiple kernel-based classifier for multi-class classification problem, is presented. We examine empirical performance of proposed approach on benchmark hyperspectral classification data set in comparison with various state-of-the-art algorithms. Experimental results show that SMKB is more effective, efficient than traditional MKL techniques.
2 citations
01 Jan 2020
TL;DR: Experimental results demonstrate that informative captions with high BLEU_3 scores can be obtained to describe images.
Abstract: Automatic caption generation from images is an interesting and mainstream direction in the field of machine learning This method enables us to build a powerful computer model that can interpret the implicit semantic information of images However, the current state of research faces significant challenges such as those related to extracting robust image features, suppressing noisy words, and improving a caption’s coherence For the first problem, a novel computer vision system is presented to create a new image feature called MK–KDES-1 (MK–KDES represents Multiple Kernel–Kernel Descriptors) after extracting three KDES features and fusing them by MKL (Multiple Kernel Learning) model The MK–KDES-1 feature captures both textural characteristics and shape characteristics of images, which contribute to improving the BLEU_1 (BLEU represents Bilingual Evaluation Understudy) scores of captions For the second problem, an effective newly designed two-layer TR (Tag Refinement) strategy is integrated into our NLG (Natural Language Generation) algorithm Words that are most relevant semantically to images are summarized to generate N-gram phrases Noisy words are suppressed using the innovative TR strategy For the last problem, on the one hand, a pop WE (Word Embeddings) model and a novel metric called PDI (Positive Distance Information) are introduced together to generate N-gram phrases The phrases are evaluated by the AWSC (Accumulated Word Semantic Correlation) metric On the other hand, the phrases are fused to generate captions by the ST (Syntactic Trees) Experimental results demonstrate that informative captions with high BLEU_3 scores can be obtained to describe images
2 citations