Add to ORKG[[abstract]]Many studies about learning in limited data were made in recent years. Without double, small data set learning is a challenging problem. Information in data of small size is scarce and has some learning limit. While discussing the learning accuracy in limited data, different classification method causes different results for different data because each classification method has its property. A method is the best solution for one data but is not the best for another. Therefore, this study analyzes the characteristics of small data set learning by the comparison of classification methods. The Mega-fuzzification method for small data set learning is applied mainly. The comparison of different classification methods for small data s...
[[abstract]]A small dataset often makes it difficult to build a reliable learning model, and thus so...
Different from deep learning with large scale supervision, few-shot learning aims to learn the sampl...
Machine learning is a popular way to find patterns and relationships in high complex datasets. With ...
Abstract:- Many studies about learning in limited data were made in recent years. Without double, sm...
Abstract. This paper reviews the appropriateness for application to large data sets of standard mach...
Learning algorithms proved their ability to deal with large amount of data. Most of the statistical ...
Being able to learn from small amounts of data is a key characteristic of human intelligence, but ex...
International audienceMany fields are now snowed under with an avalanche of data, which raises consi...
In today’s world,enormous amount of data is available in every field including science, industry, bu...
Systems that learn from examples often express the learned concept in the form of a disjunctive desc...
In text classification, providing an efficient classifier even if the number of documents involved i...
Systems that learn from examples often express the learned concept in the form of a disjunctive desc...
Abstract. In text classification, providing an efficient classifier even if the num-ber of documents...
Data mining involves the computational process to find patterns from large data sets. Classification...
Summarization: The classification problem is of major importance to a plethora of research fields. T...
[[abstract]]A small dataset often makes it difficult to build a reliable learning model, and thus so...
Different from deep learning with large scale supervision, few-shot learning aims to learn the sampl...
Machine learning is a popular way to find patterns and relationships in high complex datasets. With ...
Abstract:- Many studies about learning in limited data were made in recent years. Without double, sm...
Abstract. This paper reviews the appropriateness for application to large data sets of standard mach...
Learning algorithms proved their ability to deal with large amount of data. Most of the statistical ...
Being able to learn from small amounts of data is a key characteristic of human intelligence, but ex...
International audienceMany fields are now snowed under with an avalanche of data, which raises consi...
In today’s world,enormous amount of data is available in every field including science, industry, bu...
Systems that learn from examples often express the learned concept in the form of a disjunctive desc...
In text classification, providing an efficient classifier even if the number of documents involved i...
Systems that learn from examples often express the learned concept in the form of a disjunctive desc...
Abstract. In text classification, providing an efficient classifier even if the num-ber of documents...
Data mining involves the computational process to find patterns from large data sets. Classification...
Summarization: The classification problem is of major importance to a plethora of research fields. T...
[[abstract]]A small dataset often makes it difficult to build a reliable learning model, and thus so...
Different from deep learning with large scale supervision, few-shot learning aims to learn the sampl...
Machine learning is a popular way to find patterns and relationships in high complex datasets. With ...