Add to ORKGThe idea of local learning, classifying a particular point based on its neighbors, has been successfully applied to supervised learning problems. In this paper, we adapt it for Transductive Classification (TC) problems. Specifically, we formulate a Local Learning Regularizer (LL-Reg) which leads to a solution with the property that the label of each data point can be well predicted based on its neighbors and their labels. For model selection, an efficient way to compute the leave-one-out classification error is provided for the proposed and related algorithms. Experimental results using several benchmark datasets illustrate the effectiveness of the proposed approach
Next to prediction accuracy, the interpretability of models is one of the fundamental criteria for m...
This paper is concerned with transductive learning. Although transduction appears to be an easier ta...
In the past decades, machine learning models, especially supervised learning algorithms, have been w...
The idea of local learning, classifying a particular point based on its neighbors, has been successf...
We consider the learning problem in the transductive setting. Given a set of points of which only so...
Abstract. Semi-supervised learning has witnessed increasing interest in the past decade. One common ...
Local learning methods approximate a target function (a posteriori probability) by partitioning the ...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Abstract: It is well known that the generalization capability is one of the most important criterion...
Zero Shot Learning (ZSL) has attracted much attention due to its ability to recognize objects of uns...
This paper proposes a unified approach to learning from constraints, which integrates the ability of...
Supervised learning is investigated, when the data are represented not only by labeled points but al...
We present a new method for transductive learning, which can be seen as a transductive version of th...
In supervised learning problems, global and local learning algorithms are used. In contrast to globa...
Next to prediction accuracy, the interpretability of models is one of the fundamental criteria for m...
This paper is concerned with transductive learning. Although transduction appears to be an easier ta...
In the past decades, machine learning models, especially supervised learning algorithms, have been w...
The idea of local learning, classifying a particular point based on its neighbors, has been successf...
We consider the learning problem in the transductive setting. Given a set of points of which only so...
Abstract. Semi-supervised learning has witnessed increasing interest in the past decade. One common ...
Local learning methods approximate a target function (a posteriori probability) by partitioning the ...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Abstract: It is well known that the generalization capability is one of the most important criterion...
Zero Shot Learning (ZSL) has attracted much attention due to its ability to recognize objects of uns...
This paper proposes a unified approach to learning from constraints, which integrates the ability of...
Supervised learning is investigated, when the data are represented not only by labeled points but al...
We present a new method for transductive learning, which can be seen as a transductive version of th...
In supervised learning problems, global and local learning algorithms are used. In contrast to globa...
Next to prediction accuracy, the interpretability of models is one of the fundamental criteria for m...
This paper is concerned with transductive learning. Although transduction appears to be an easier ta...
In the past decades, machine learning models, especially supervised learning algorithms, have been w...