Add to ORKGIn 1988, Langley wrote an influential editorial in the journal Machine Learning titled “Machine Learning as an Experimental Science”, arguing persuasively for a greater focus on performance testing. Since that time the emphasis has become progressively stronger. Nowadays, to be accepted to one of our major conferences or journals, a paper must typically contain a large experimental section with many tables of results, concluding with a statistical test. In revisiting this paper, I claim that we have ignored most of its advice. We have focused largely on only one aspect, hypothesis testing, and a narrow version at that. This version provides us with evidence that is much more impoverished than many people realize. I argue that such tests are...
Machine learning research often has a large experimental component. While the experimental methodolo...
Significance testing has become a mainstay in machine learning, with the p value being firmly embedd...
International audienceScience has progressed by reasoning on what models could not predict because t...
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 \u201cMachine...
One of the greatest machine learning prob-lems of today is an intractable number of new algorithms b...
Developing state-of-the-art approaches for specific tasks is a major driving force in our research c...
Machine Learning (ML), or the ability of self-learning computer algorithms to autonomously structure...
Learning is constructing or modifying representations of what is being experi-enced. and then using ...
In our work, we have explored the principles used in machine learning and a set of applications of m...
suggests a reasonable line of research: find algorithms that can search the hypothesis class better....
As its name suggests, articial intelligence is a science of the articial (Simon, 1969). As with othe...
Thousands of Machine Learning research papers contain experimental comparisons that usually have bee...
This paper gives an overview of some ways in which our understanding of performance evaluation measu...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Machine learning research often has a large experimental component. While the experimental methodolo...
Significance testing has become a mainstay in machine learning, with the p value being firmly embedd...
International audienceScience has progressed by reasoning on what models could not predict because t...
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 \u201cMachine...
One of the greatest machine learning prob-lems of today is an intractable number of new algorithms b...
Developing state-of-the-art approaches for specific tasks is a major driving force in our research c...
Machine Learning (ML), or the ability of self-learning computer algorithms to autonomously structure...
Learning is constructing or modifying representations of what is being experi-enced. and then using ...
In our work, we have explored the principles used in machine learning and a set of applications of m...
suggests a reasonable line of research: find algorithms that can search the hypothesis class better....
As its name suggests, articial intelligence is a science of the articial (Simon, 1969). As with othe...
Thousands of Machine Learning research papers contain experimental comparisons that usually have bee...
This paper gives an overview of some ways in which our understanding of performance evaluation measu...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Machine learning research often has a large experimental component. While the experimental methodolo...
Significance testing has become a mainstay in machine learning, with the p value being firmly embedd...
International audienceScience has progressed by reasoning on what models could not predict because t...