Add to ORKGWe consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive in-ference. A principled approach to semi-supervised learning is to design a classifying function which is sufciently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of clas-sication problems and demonstrates effective use of unlabeled data.
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use o...
We propose a unified perspective of a large family of semi-supervised learning algorithms, which sel...
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...
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...
We consider the general problem of learning from labeled and unlabeled data, which is often called...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the learning problem in the transductive setting. Given a set of points of which only so...
We consider the learning problem in the transductive setting. Given a set of points of which only so...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Abstract. Semi-supervised learning and active learning are important techniques to solve the shortag...
The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use o...
We propose a unified perspective of a large family of semi-supervised learning algorithms, which sel...
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...
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...
We consider the general problem of learning from labeled and unlabeled data, which is often called...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
We consider the learning problem in the transductive setting. Given a set of points of which only so...
We consider the learning problem in the transductive setting. Given a set of points of which only so...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Abstract. Semi-supervised learning and active learning are important techniques to solve the shortag...
The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training...
In the supervised learning the data are divided into training set and unclassified set. A classifier...
Semi-supervised learning is the learning setting in which we have both labeled and unlabeled data at...
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use o...
We propose a unified perspective of a large family of semi-supervised learning algorithms, which sel...