Self-adaptive Support Vector Machine: A multi-agent optimization perspective

Couellan, Nicolas; Jan, Sophie; Jorquera, Tom; Georgé, Jean‐Pierre

doi:10.1016/j.eswa.2015.01.028

Cited by 33 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are various techniques are available for document classification like, Naïve Bayes, KNN, Expectation Maximization, SVM, Decision Trees. Support Vector machines is one of the learning system for desirable qualities with most common algorithms [8]. SVM is the main classifier algorithm for text classification.…”

Section: Introductionmentioning

confidence: 99%

A technical study on Information Retrieval using web mining techniques

Sathiaseelan¹

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

View full text Add to dashboard Cite

Information Retrieval (IR) is a significant area in web mining where the users procure their needed information from the web. The documents in the web are in the form of web pages. As there is a high usage of data in the web, researchers are intended to do research in the field of IR. Web query classification and web page classification are leading major role to classify the web documents in IR using classification algorithms, based on the user query. The classified documents are indexed using ranking algorithm. This research paper aims to study about various methods of IR to classify and index the relevant web pages respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

A technical study on Information Retrieval using web mining techniques

Sathiaseelan¹

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

View full text Add to dashboard Cite

show abstract

“…Our work is motivated by appealing features of a MAS which could be advantageously used to elaborate intelligent computing systems (Martin, Ouelhadj, Smetb, Beullens, &Özcan, 2013;Guo, Goncalves, & Hsu, 2013;Gonçalves, Guimarães, & Souza, 2014;Satunin & Babkin, 2014;Couellan, Jan, Jorquera, & Georgé, 2015;Baykasoglu & Kaplanoglu, 2015;Zheng & Wang, 2015). Compared with the existing studies on the QAP, this work has the following main contributions:…”

Section: Introductionmentioning

confidence: 99%

“…The solutions obtained by using the proposed approach demonstrate the usefulness of the approach in providing high-quality solutions while generating real-time schedules. Couellan, Jan, Jorquera, & Georgé (2015) are interested in solving challenging optimization problems raised in training problems of Support Vector Machines (SVM). They observe that multi-agents systems are able to break down a complex optimization problem into elementary oracle tasks which are solved by performing a collaborative solution process.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-agent based optimization method applied to the quadratic assignment problem

Sghir

Hao

Jaâfar

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

Inspired by the idea of interacting intelligent agents of a multi-agent system, we introduce a multi-agent based optimization method applied to the quadratic assignment problem (MAOM-QAP). MAOM-QAP is composed of several agents (decision-maker agent, local search agents, crossover agents and perturbation agent) which are designed for the purpose of intensified and diversified search activities. With the help of a reinforcement learning mechanism, MAOM-QAP dynamically decides the most suitable agent to activate according to the state of search process. Under the coordination of the decision-maker agent, the other agents fulfill dedicated search tasks. The performance of the proposed approach is assessed on the set of well-known QAP benchmark instances, and compared with the most advanced QAP methods of the literature. The ideas proposed in this work are rather general and could be adapted to other optimization tasks. This work opens the way for designing new distributed intelligent systems for tackling other complex search problems.

show abstract

“…Some specialized cases on the use of neural networks and stochastic models are reported in (Amaya et al 2007;Araujo and Bezerra 2004;Figueiredo 2009;Gurney 1997;Huang andYao 2008, Luna et al 2006;Marco et al 2010, Múller 2007Vujanovic et al 2012 andZhao 2009). Other tools that may contribute to the development of new models for the optimization of replacement vehicles include fuzzy logic and the Support Vector Machine (SVM), (Tsoukalas and Uhrig 1996;Yager and Zadeh 1992;Campello and Amaral 2001;Couellan et al 2015;Chen et al 2015;Pooyan et al 2015).…”

Section: State Of the Art Condition Monitoringmentioning

confidence: 99%

Dimensioning reserve bus fleet using life cycle cost models and condition based/predictive maintenance: a case study

Raposo¹,

Farinha²,

Ferreira³

et al. 2017

Public Transp

View full text Add to dashboard Cite

The paper demonstrates the dependence of a fleet reserve of buses on the maintenance policy of the whole fleet, in particular, condition-based maintenance using a motor oil degradation analysis. The paper discusses an approach to evaluate the oil degradation and the prediction of the next value for one relevant oil variable. The methodology to evaluate the reserve fleet is based on bus availability, estimated through the mean time between failures and the mean time to repair ratios. Through the use of econometric models, it is possible to determine the most rational size of the reserve fleet.

show abstract

Self-adaptive Support Vector Machine: A multi-agent optimization perspective

Cited by 33 publications

References 13 publications

A technical study on Information Retrieval using web mining techniques

A technical study on Information Retrieval using web mining techniques

A multi-agent based optimization method applied to the quadratic assignment problem

Dimensioning reserve bus fleet using life cycle cost models and condition based/predictive maintenance: a case study

Contact Info

Product

Resources

About