Add to ORKGOne of the core applications of machine learning to knowledge discovery consists on building a function (a hypothesis) from a given amount of data (for instance a decision tree or a neural network) such that we can use it afterwards to predict new instances of the data. In this paper, we focus on a particular situation where we assume that the hypothesis we want to use for prediction is very simple, and thus, the hypotheses class is of feasible size. We study the problem of how to determine which of the hypotheses in the class is almost the best one. We present two on-line sampling algorithms for selecting hypotheses, give theoretical bounds on the number of necessary examples, and analize them exprimentally. We compare them with ...
Editor: Abstract. We introduce a new formal model in which a learning algorithm must combine a colle...
Abstract. The areas of On-Line Algorithms and Machine Learning are both concerned with problems of m...
The capability of effectively quantifying the uncertainty associated to a given prediction is an imp...
One of the core applications of machine learning to knowledge discovery consists on building a funct...
consists on building a function (a hypothesis) from a given amount of data (for instance a decision...
Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as n-be...
This note presents a complete and correct theoretical analysis of the algorithms for hypothesis sele...
In this paper we show how to extract a hypothesis with small risk from the ensemble of hypotheses ge...
The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making deci...
Abstract. We introduce a new formal model in which a learning algorithm must combine a collection of...
One of the fundamental machine learning tasks is that of predictive classification. Given that organ...
Given k pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide ...
The ability to handle and analyse massive amounts of data has been progressively improved during the...
Importance sampling, a variant of online sampling, is often used in neural network training to impro...
One of the biggest research challenges in KDD and Data Mining is to develop methods that scale up w...
Editor: Abstract. We introduce a new formal model in which a learning algorithm must combine a colle...
Abstract. The areas of On-Line Algorithms and Machine Learning are both concerned with problems of m...
The capability of effectively quantifying the uncertainty associated to a given prediction is an imp...
One of the core applications of machine learning to knowledge discovery consists on building a funct...
consists on building a function (a hypothesis) from a given amount of data (for instance a decision...
Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as n-be...
This note presents a complete and correct theoretical analysis of the algorithms for hypothesis sele...
In this paper we show how to extract a hypothesis with small risk from the ensemble of hypotheses ge...
The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making deci...
Abstract. We introduce a new formal model in which a learning algorithm must combine a collection of...
One of the fundamental machine learning tasks is that of predictive classification. Given that organ...
Given k pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide ...
The ability to handle and analyse massive amounts of data has been progressively improved during the...
Importance sampling, a variant of online sampling, is often used in neural network training to impro...
One of the biggest research challenges in KDD and Data Mining is to develop methods that scale up w...
Editor: Abstract. We introduce a new formal model in which a learning algorithm must combine a colle...
Abstract. The areas of On-Line Algorithms and Machine Learning are both concerned with problems of m...
The capability of effectively quantifying the uncertainty associated to a given prediction is an imp...