Add to ORKGOne of the greatest machine learning prob-lems of today is an intractable number of new algorithms being presented at our con-ferences, workshops and journals. A similar rush of ideas and results also plagues most other scientific fields and some have already questioned the usefulness of statistical tests for telling the true relations from the false. Statistical tests have been criticized as con-ceptually wrong almost from their inception. They do not work well in situations when nu-merous groups conduct similar research. Not measuring what we are really interested in, they can promote the randomly successfu
In spite of the widespread use of significance testing in empirical research, its interpretation and...
This paper reviews five statistical tests for determining whether one learning algorithm outperforms...
Machine Learning is a branch of artificial intelligence focused on building applications that learn ...
In 1988, Langley wrote an influential editorial in the jour-nal Machine Learning titled “Machine Lea...
In 1988, Langley wrote an influential editorial in the journal Machine Learning titled “Machine Lear...
In 1988, Langley wrote an influential editorial in the journal Machine Learning titled \u201cMachine...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Machine learning and statistics are one and the same discipline, with different communities of resea...
Developing state-of-the-art approaches for specific tasks is a major driving force in our research c...
This article reviews five approximate statistical tests for determining whether one learning algorit...
We initiate a computational theory of statistical tests. Loosely speaking, we say that an algorithm ...
Significance testing has become a mainstay in machine learning, with the p value being firmly embedd...
The evaluation of classifiers or learning algorithms is not a topic that has, generally, been given ...
Despite being a standard tool for data analysis in many scientific fields, statistical testing has a...
Recent improvements in machine learning methods have significantly advanced many fields in- cluding ...
In spite of the widespread use of significance testing in empirical research, its interpretation and...
This paper reviews five statistical tests for determining whether one learning algorithm outperforms...
Machine Learning is a branch of artificial intelligence focused on building applications that learn ...
In 1988, Langley wrote an influential editorial in the jour-nal Machine Learning titled “Machine Lea...
In 1988, Langley wrote an influential editorial in the journal Machine Learning titled “Machine Lear...
In 1988, Langley wrote an influential editorial in the journal Machine Learning titled \u201cMachine...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Machine learning and statistics are one and the same discipline, with different communities of resea...
Developing state-of-the-art approaches for specific tasks is a major driving force in our research c...
This article reviews five approximate statistical tests for determining whether one learning algorit...
We initiate a computational theory of statistical tests. Loosely speaking, we say that an algorithm ...
Significance testing has become a mainstay in machine learning, with the p value being firmly embedd...
The evaluation of classifiers or learning algorithms is not a topic that has, generally, been given ...
Despite being a standard tool for data analysis in many scientific fields, statistical testing has a...
Recent improvements in machine learning methods have significantly advanced many fields in- cluding ...
In spite of the widespread use of significance testing in empirical research, its interpretation and...
This paper reviews five statistical tests for determining whether one learning algorithm outperforms...
Machine Learning is a branch of artificial intelligence focused on building applications that learn ...