Add to ORKGMachine learning usually assumes that attribute values, as well as class labels, are either known precisely or not known at all. However, in our attempt to automate evaluation of intrusion detection systems, we have encountered ambigu-ous examples such that, for instance, an attribute’s value in a given example is known to be a or b but definitely not c or d. Previous research usually either ”disambiguated” the value by giving preference to a or b, or just replaced it with a ”don’t-know ” symbol. Disliking both of these two ap-proaches, we decided to explore the behavior of other ways to address the situation. To keep the work focused, we limited ourselves to nearest-neighbor classifiers. The paper describes a few techniques and reports rel...
This paper introduces a new technique for feature selection and illustrates it on a real data set. N...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
This paper introduces a new technique for feature selection and illustrates it on a real data set. N...
The importance of attribute vector ambiguity has been largely overlooked by the machine learning com...
Abstract. Inducing a classification function from a set of examples in the form of labeled instances...
Machine learning has an important role in many computer vision applications, including object detect...
A key requirement for supervised machine learning is labeled training data, which is created by anno...
This Open access is brought to you for free and open access by the Electronic Theses and Dissertatio...
Most performance metrics for learning algorithms do not provide information about the misclassified ...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
AbstractThis paper presents average-case analyses of instance-based learning algorithms. The algorit...
Modern supervised learning algorithms can learn very accurate and complex discriminating functions. ...
The 1R procedure for machine learning is a very simple one that proves surprisingly effective on the...
The nearest neighbour paradigm provides an effective approach to supervised learning. However, it is...
In this paper, we study a special kind of learning problem in which each training instance is given ...
This paper introduces a new technique for feature selection and illustrates it on a real data set. N...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
This paper introduces a new technique for feature selection and illustrates it on a real data set. N...
The importance of attribute vector ambiguity has been largely overlooked by the machine learning com...
Abstract. Inducing a classification function from a set of examples in the form of labeled instances...
Machine learning has an important role in many computer vision applications, including object detect...
A key requirement for supervised machine learning is labeled training data, which is created by anno...
This Open access is brought to you for free and open access by the Electronic Theses and Dissertatio...
Most performance metrics for learning algorithms do not provide information about the misclassified ...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
AbstractThis paper presents average-case analyses of instance-based learning algorithms. The algorit...
Modern supervised learning algorithms can learn very accurate and complex discriminating functions. ...
The 1R procedure for machine learning is a very simple one that proves surprisingly effective on the...
The nearest neighbour paradigm provides an effective approach to supervised learning. However, it is...
In this paper, we study a special kind of learning problem in which each training instance is given ...
This paper introduces a new technique for feature selection and illustrates it on a real data set. N...
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors...
This paper introduces a new technique for feature selection and illustrates it on a real data set. N...