Add to ORKGA teacher provides the value for the target function for all training examples (labeled examples) concept learning, classification, regression Unsupervised Learning There is no information except the training examples clustering, subgroup discovery, association rule discovery Reinforcement Learning The teacher only provides feedback but not example values Semi-supervised Learning Only a subset of the training examples are labele
The learning model of Valiant is extended to allow the number of examples required for learning to d...
Teaching with evaluative feedback involves expectations about how a learner will interpret rewards a...
Abstract. Within learning theory teaching has been studied in various ways. In a common variant the ...
The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training...
Teaching is challenging in a real environment. One problem is that not all examples may be available...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Traditional machine learning algorithms have failed to serve the needs of systems for Programming by...
How to train an ideal teacher for knowledge distillation is still an open problem. It has been widel...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
The majority of existing few-shot learning methods describe image relations with binary labels. Howe...
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use o...
Teaching involves a mixture of instruction, self-studying in the absence of a teacher and assessment...
Abstract. Traditional active learning allows a (machine) learner to query the (human) teacher for la...
In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instanc...
The learning model of Valiant is extended to allow the number of examples required for learning to d...
Teaching with evaluative feedback involves expectations about how a learner will interpret rewards a...
Abstract. Within learning theory teaching has been studied in various ways. In a common variant the ...
The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training...
Teaching is challenging in a real environment. One problem is that not all examples may be available...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Traditional machine learning algorithms have failed to serve the needs of systems for Programming by...
How to train an ideal teacher for knowledge distillation is still an open problem. It has been widel...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
The majority of existing few-shot learning methods describe image relations with binary labels. Howe...
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use o...
Teaching involves a mixture of instruction, self-studying in the absence of a teacher and assessment...
Abstract. Traditional active learning allows a (machine) learner to query the (human) teacher for la...
In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instanc...
The learning model of Valiant is extended to allow the number of examples required for learning to d...
Teaching with evaluative feedback involves expectations about how a learner will interpret rewards a...
Abstract. Within learning theory teaching has been studied in various ways. In a common variant the ...