Robust and efficient multiclass SVM models for phrase pattern recognition

Wu, Yu-Chieh; Lee, Yue‐Shi; Yang, Jie Chi

doi:10.1016/j.patcog.2008.02.010

Cited by 72 publications

(24 citation statements)

References 19 publications

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“…It is a class of kernel-based learning methods [33]. Now, the LSSVM has been widely used in forecasting [34][35][36], data fitting [37,38], comprehensive evaluation [39,40] and pattern recognition [41][42][43]. The steps are as follows.…”

Section: The Topsis Improved By the Grey Incidence Analysismentioning

confidence: 99%

Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

Niu

Dai

et al. 2018

Sustainability

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Abstract:The electric power industry is of great significance in promoting social and economic development and improving people's living standards. Power grid construction is a necessary part of infrastructure construction, whose sustainability plays an important role in economic development, environmental protection and social progress. In order to effectively evaluate the sustainability of power grid construction projects, in this paper, we first identified 17 criteria from four dimensions including economy, technology, society and environment to establish the evaluation criteria system. After that, the grey incidence analysis was used to modify the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), which made it possible to evaluate the sustainability of electric power construction projects based on visual angle of similarity and nearness. Then, in order to simplify the procedure of experts scoring and computation, on the basis of evaluation results of the improved TOPSIS, the model using Modified Fly Optimization Algorithm (MFOA) to optimize the Least Square Support Vector Machine (LSSVM) was established. Finally, a numerical example was given to demonstrate the effectiveness of the proposed model.

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Section: The Topsis Improved By the Grey Incidence Analysismentioning

confidence: 99%

Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

Niu

Dai

et al. 2018

Sustainability

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“…It has been used in various applications such as text classification [5], facial expression recognition [9], gene analysis [4] and many others [1,6,7,8,10,11,12,17,19,20,21,22]. Recently, Wang et al [15] presented SVM based fault classifier design for a water level control system.…”

Section: Support Vector Machinementioning

confidence: 99%

Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines

Nazır

Silvestrov

2016

Springer Proceedings in Mathematics &Amp; Statistics

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In this paper, we study the support vector machine and introduced the notion of generalized support vector machine for classification of data. We show that the problem of generalized support vector machine is equivalent to the problem of generalized variational inequality and establish various results for the existence of solutions. Moreover, we provide various examples to support our results.---------------

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“…Wu et al [19] presented an automatic chunk pair relation construction algorithm which can handle so-called IOB1/IOB2/IOE1/IOE2 [7 17] chunk representation structures with either left-to-right or right-to-left directions. However, the main limitation is that it only works well on IOB or IOE tags rather than more complex phrase structures, for example SBIE.…”

Section: Automatic Chunk Relation Constructionmentioning

confidence: 99%

“…Support vector machines (SVMs) which is one of the state-of-the-art supervised learning algorithms have been widely employed as local classifiers to many sequential labeling tasks [7,5,19]. In particular, the learning time of linear kernel SVM can now be trained in linear time [4].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Deterministic and Nondeterministic Inference Algorithm for Sequential Labeling

Lee

Yang

et al. 2010

Information Retrieval Technology

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Abstract. In this paper, we present a new search algorithm for sequential labeling tasks based on the conditional Markov models (CMMs) frameworks. Unlike conventional beam search, our method traverses all possible incoming arcs and also considers the "local best" so-far of each previous node. Furthermore, we propose two heuristics to fit the efficiency requirement. To demonstrate the effect of our method, six variant and large-scale sequential labeling tasks were conducted in the experiment. In addition, we compare our method to Viterbi and Beam search approaches. The experimental results show that our method yields not only substantial improvement in runtime efficiency, but also slightly better accuracy. In short, our method achieves 94.49 F (β) rate in the well-known CoNLL-2000 chunking task.

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Robust and efficient multiclass SVM models for phrase pattern recognition

Cited by 72 publications

References 19 publications

Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines

An Integrated Deterministic and Nondeterministic Inference Algorithm for Sequential Labeling

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