This paper reviews the appropriateness for application to large data sets of standard machine learning algorithms, which were mainly developed in the context of small data sets. Sampling and parallelisation have proved useful means for reducing computation time when learning from large data sets. However, such methods assume that algorithms that were designed for use with what are now considered small data sets are also fundamentally suitable for large data sets. It is plausible that optimal learning from large data sets requires a different type of algorithm to optimal learning from small data sets. This paper investigates one respect in which data set size may affect the requirements of a learning algorithm — the bias plus variance ...
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
The last few years have witnessed the rise of the big data era, which features the prevalence of dat...
Recent world events in go games between human and artificial intelligence called AlphaGo showed the ...
Abstract. This paper reviews the appropriateness for application to large data sets of standard mach...
One of the fundamental machine learning tasks is that of predictive classification. Given that organ...
Traditional machine learning has been largely concerned with developing techniques for small or mode...
University of Technology Sydney. Faculty of Engineering and Information Technology.Machine learning ...
The last several years have seen the emergence of datasets of an unprecedented scale, and solving va...
Pervasive and networked computers have dramatically reduced the cost of collecting and distributing ...
We study the performance of Machine Learning (ML) classification techniques. Leveraging the theory o...
Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science,...
In the field of machine learning classification is one of the most common types to be deployed in so...
Machine Learning is a branch of artificial intelligence focused on building applications that learn ...
The availability of big data sets in research, industry and society in general has opened up many po...
AbstractThe studies of generalization error give possible approaches to estimate the performance of ...
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
The last few years have witnessed the rise of the big data era, which features the prevalence of dat...
Recent world events in go games between human and artificial intelligence called AlphaGo showed the ...
Abstract. This paper reviews the appropriateness for application to large data sets of standard mach...
One of the fundamental machine learning tasks is that of predictive classification. Given that organ...
Traditional machine learning has been largely concerned with developing techniques for small or mode...
University of Technology Sydney. Faculty of Engineering and Information Technology.Machine learning ...
The last several years have seen the emergence of datasets of an unprecedented scale, and solving va...
Pervasive and networked computers have dramatically reduced the cost of collecting and distributing ...
We study the performance of Machine Learning (ML) classification techniques. Leveraging the theory o...
Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science,...
In the field of machine learning classification is one of the most common types to be deployed in so...
Machine Learning is a branch of artificial intelligence focused on building applications that learn ...
The availability of big data sets in research, industry and society in general has opened up many po...
AbstractThe studies of generalization error give possible approaches to estimate the performance of ...
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
The last few years have witnessed the rise of the big data era, which features the prevalence of dat...
Recent world events in go games between human and artificial intelligence called AlphaGo showed the ...