首页|Data on Machine Learning Detailed by Researchers at Instituto Politecnico de Braganca (Hybrid Approaches To Optimization and Machine Learning Methods: a Systematic Literature Review)
Data on Machine Learning Detailed by Researchers at Instituto Politecnico de Braganca (Hybrid Approaches To Optimization and Machine Learning Methods: a Systematic Literature Review)
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Springer Nature
Investigators discuss new findings in Machine Learning. According to news reporting out of Braganca, Portugal, by NewsRx editors, research stated, “Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies.” Financial support for this research came from Instituto Politcnico de Bragana. Our news journalists obtained a quote from the research from Instituto Politecnico de Braganca, “In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods.”
BragancaPortugalEuropeAlgorithmsCyborgsEmerging TechnologiesMachine LearningInstituto Politecnico de Braganca
NETL
NSTL
Springer Nature
