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Convergence of a generalized smo algorithm for SVM classifier design

MACHINE LEARNING 2002年第1-3期46卷 351-360页

作者： Keerthi, SS Gilbert, EG Natl Univ Singapore Dept Mech Engn Singapore 119260 Singapore Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

Convergence of a generalized version of the modified smo algorithms given by Keerthi et al. for SVM classifier design is proved. The convergence results are also extended to modified smo algorithms for solving nu -SVM... 详细信息

关键词： support vector machine smo algorithm convergence

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Sports combination training action recognition based on smo algorithm optimization model

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-12页

作者： Zhang, Jianye Tianjin Univ Commerce Tianjin 300134 Peoples R China

In sports combined training, the artificial intelligence recognition system can effectively improve the training effect, but the impact of complex background will lead to lower recognition accuracy. In order to improve the effect of artificial intelligence recognition, this paper builds a model based on machine learning algorithms and improves the smo algorithm. Moreover, this paper uses sequence minimization algorithm (smo) to optimize the support vector machine (SVM) model and establishes an improved support vector machine model (smo-SVM) based on the smo algorithm. In addition, in sports training, this article uses computer vision recognition technology to perform three-dimensional modeling and detection of wrong actions to achieve high detection accuracy to effectively control the detection errors, so that the wrong actions can be judged timely and accurately. Finally, this paper uses BP neural network to detect key poses of sports videos and establishes feature parameters that can express image information through deep learning methods. The experimental results show that the model constructed in this paper meets actual needs.

关键词： smo algorithm Optimization model Sports combination training Action recognition

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Recognizing ion ligand binding sites by smo algorithm

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BMC MOLECULAR AND CELL BIOLOGY 2019年第3-Sup期20卷 53-53页

作者： Wang, Shan Hu, Xiuzhen Feng, Zhenxing Zhang, Xiaojin Liu, Liu Sun, Kai Xu, Shuang Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China

Background: In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results: In this study, four acid radical ion ligands (NO2-,CO32-,SO42-,PO43-) and ten metal ion ligands (Zn2+,Cu2+, Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (smo) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions: An efficient method for predicting ion ligand binding sites was presented.

关键词： Ion ligand smo algorithm Binding site Sequence information

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Support Vector Machine smo algorithms and Their Optimization

Support Vector Machine SMO Algorithms and Their Optimization

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3rd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2019)

作者： Xiujun Wu School of Computer Science and Technology Wuhan University of Technology

Support Vector Machine(SVM) is an important statistical machine learning algorithm, among which smo algorithm is one of the effective application methods. smo algorithm decomposes the problem of support vector machine into smaller problems to minimize the sequence. Through the introduction of smo algorithm and the analysis and summary of the problems, this paper explores the corresponding smo optimization algorithm, hoping to provide theoretical support for future research.

关键词： support vector machine smo algorithm Optimization Research

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L₂ Kernel Classification

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010年第10期32卷 1822-1831页

作者： Kim, JooSeuk Scott, Clayton D. Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Nonparametric kernel methods are widely used and proven to be successful in many statistical learning problems. Well-known examples include the kernel density estimate (KDE) for density estimation and the support vector machine (SVM) for classification. We propose a kernel classifier that optimizes the L-2 or integrated squared error (ISE) of a "difference of densities." We focus on the Gaussian kernel, although the method applies to other kernels suitable for density estimation. Like a support vector machine (SVM), the classifier is sparse and results from solving a quadratic program. We provide statistical performance guarantees for the proposed L-2 kernel classifier in the form of a finite sample oracle inequality and strong consistency in the sense of both ISE and probability of error. A special case of our analysis applies to a previously introduced ISE-based method for kernel density estimation. For dimensionality greater than 15, the basic L-2 kernel classifier performs poorly in practice. Thus, we extend the method through the introduction of a natural regularization parameter, which allows it to remain competitive with the SVM in high dimensions. Simulation results for both synthetic and real-world data are presented.

关键词： Kernel methods sparse classifiers integrated squared error difference of densities smo algorithm

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Analysis on improving the application of machine learning in product development

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JOURNAL OF SUPERCOMPUTING 2022年第10期78卷 12435-12460页

作者： Chang, Yu-Teng Yang, Hui-Ru Chen, Chien-Ming Yu Da Univ Sci & Technol Dept Informat Management Miaoli 36143 Taiwan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

One of the main competitive advantages of an enterprise is its continuous development of new products. The higher the level of product innovation, the more the enterprise is ahead of its competitors. However, products that are more innovative have a higher chance of failure. Traditionally, enterprises use questionnaires, focus groups and customer interviews to understand the expectations and requirements of their customers for specific products;however, if the customers do not fully understand their own needs, they cannot express them fully. Therefore, in order to understand the functional requirements that are expected from their products, some enterprises go directly to the place where their customers are and observe how they use them. Nevertheless, the success rate for new product development is low. To solve this problem, this study uses the machine learning algorithm for the planning and prediction of product R&D. Firstly, the prediction model of a product's success rate is established by using the random tree algorithm, the bagging algorithm and the smo algorithm. The model is then used for the planning and prediction of new products. The results show that the model accuracy of the random tree algorithm is 100%, that of the bagging algorithm is 83.33%, and that of the smo algorithm is 91.67%. The prediction results of the three algorithms are integrated to judge the market's acceptance of the planned new products. Finally, the new products are improved, and after the improvements have been made, the three algorithms are used to make predictions. As a result, the acceptance of new products by customers has increased significantly. According to this study, machine learning can improve the success rate of new products in the marketplace.

关键词： Machine learning Product development Random tree algorithm Bagging algorithm smo algorithm

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SVM classification of human intergenic and gene sequences

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MATHEMATICAL BIOSCIENCES 2005年第2期195卷 168-178页

作者： Qiao, YH Liu, JL Zhang, CG Xu, XH Zeng, YJ Beijing Univ Technol Biomech & Med Informat Inst Beijing 100022 Peoples R China Beijing Univ Technol Coll Comp Sci Beijing 100022 Peoples R China Beijing Inst Radiat Beijing 100850 Peoples R China Shan Tou Univ Sch Med Guangdong 515031 Peoples R China

Despite constant improvement in prediction accuracy, gene-finding programs are still unable to provide automatic gene discovery with the desired correctness. This paper presents an analysis of gene and intergenic sequences from the point of view of language analysis, where gene and intergenic regions are regarded as two different subjects written in the four-letter alphabet {A,C,G,T}, and high frequency simple sequences are taken as keywords. A measurement alpha(l(tau)) was introduced to describe the relative repeat ratio of simple sequences. Threshold values were found for keyword selections. After eliminating 'noise', 178 short sequences were selected as keywords. DNA sequences are mapped to 178-dimensional Euclidean space, and SVM was used for prediction of gene regions. We showed by cross-validation that the program we developed could predict 93% of gene sequences with 7% false positives. When tested on a long genomic multi-gene sequence, our method improved nucleotide level specificity by 21%, and over 60% of predicted genes corresponded to actual genes. (c) 2005 Elsevier Inc. All rights reserved.

关键词： SVM smo algorithm intergenic region gene region

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An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1799-1819页

作者： Amer, Dina A. Attiya, Gamal Ziedan, Ibrahim Higher Technol Inst Comp Sci Dept Tenth Of Ramadan City Egypt Menoufia Univ Fac Elect Engn Comp Sci & Engn Dept Menoufia Egypt Zagazig Univ Fac Engn Comp & Syst Engn Dept Zagazig Egypt

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users' jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called smo_ACO, for addressing the scheduling problem. The proposed smo_ACO algorithm combines Spider Monkey Optimization (smo) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed smo_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.

关键词： Cloud computing Multi-objective constraints Task Scheduling smo algorithm

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A fast dual algorithm for kernel logistic regression

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MACHINE LEARNING 2005年第1-3期61卷 151-165页

作者： Keerthi, SS Duan, KB Shevade, SK Poo, AN Yahoo Res Labs Pasadena CA 91105 USA Natl Univ Singapore Dept Mech Engn Control Div Singapore Singapore Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

关键词： classification logistic regression kernel methods smo algorithm

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A developed framework for multi-document summarization using softmax regression and spider monkey optimization methods

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SOFT COMPUTING 2022年第7期26卷 3313-3328页

作者： Wilson, Praveen K. Jeba, J. R. Noorul Islamic Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil India Noorul Islamic Ctr Higher Educ Dept Comp Applicat Kumaracoil India

In everyday life, multi-document summarization (MDS) methods are becoming tremendous attention in different fields, especially for online documents, because this online document conveys information to users by generating succinct and comprehensive summary. The summarized document contains the summary of various documents with same topic. Here, Spider Monkey Optimization (smo) algorithm is introduced for summary generation. Before that, multi-documents are compressed into single document and different pre-processing methods are applied to remove the unwanted word from the document. Then, semantic and syntactic features are extracted from the document using different methods. The mined features are then provided into the softmax regression (SR) technique for further processing. Finally, smo algorithm is proposed to generate the summary about whole document. The proposed text summarization process is implemented in Python platform using the BBC news dataset, DUC (Document Understanding Conference) 2002, 2006, and 2007 datasets. During pre-processing, the tokenization is performed by Natural Language Tool Kit (NLTK) tool and the lemmatization in WordNet lemmatizer. The terms recall, F-measure and precision are offered in this work for performance evaluation, and the accuracy of this method is found to be better than the other existing MDS techniques.

关键词： Multi-document summarization (MDS) Syntactic features Semantic features Softmax regression smo algorithm

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