Introduction to Semi-Supervised Learning

@inproceedings{Chapelle2006IntroductionTS,
  title={Introduction to Semi-Supervised Learning},
  author={Olivier Chapelle and Bernhard Sch{\"o}lkopf and Alexander Zien},
  booktitle={Semi-Supervised Learning},
  year={2006}
}
This chapter contains sections titled: Supervised, Unsupervised, and Semi-Supervised Learning, When Can Semi-Supervised Learning Work?, Classes of Algorithms and Organization of This Book 
A Multi-view Regularization Method for Semi-supervised Learning
TLDR
This paper considers the regularization problem in multi-view semi-supervised learning and proposes a regularization method adaptive to the given data, which can use unlabeled data to adjust the degree of regularization automatically. Expand
Semi-supervised Learning for Multi-target Regression
TLDR
This work states that in many practical problems, the availability of annotated data is limited due to the expensive, tedious and time-consuming annotation procedure, which is especially pronounced for predictive modelling problems with a structured output space and complex labels. Expand
A Non-parametric Semi-supervised Discretization Method
TLDR
A new semi-supervised discretization method is proposed which adopts very low informative prior on data, which discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes. Expand
Semi-supervised incremental learning
The paper introduces a hybrid evolving architecture for dealing with incremental learning. It consists of two components: resource allocating neural network (RAN) and growing Gaussian mixture modelExpand
Semi-supervised feature selection under logistic I-RELIEF framework
TLDR
The basic idea of the proposed algorithm is to modify the objective function of Logistic I-RELIEF to include the margins of unlabeled samples by following the large margin principle. Expand
An efficient algorithm for large-scale quasi-supervised learning
  • B. Karaçali
  • Computer Science
  • Pattern Analysis and Applications
  • 2014
TLDR
A novel formulation for quasi-supervised learning that extends the learning paradigm to large datasets by partitions the data into sample groups to compute the dataset posterior probabilities in a smaller computational complexity. Expand
A unified semi-supervised dimensionality reduction framework for manifold learning
We present a general framework of semi-supervised dimensionality reduction for manifold learning which naturally generalizes existing supervised and unsupervised learning frameworks which apply theExpand
Semi-Supervised Regression and System Identification
System Identification and Machine Learning are developing mostly as independent subjects, although the underlying problem is the same: To be able to associate “outputs” with “inputs”. ParticularExpand
Spectral Methods for Linear and Non-Linear Semi-Supervised Dimensionality Reduction
TLDR
A general framework of spectral methods for semi-supervised dimensionality reduction by applying an approach called manifold regularization, which naturally generalizes existent supervised frameworks and can be kernelized as well. Expand
The information regularization framework for semi-supervised learning
TLDR
A unified framework that encompasses many of the common approaches to semi-supervised learning, including parametric models of incomplete data, harmonic graph regularization, redundancy of sufficient features (co-training), and combinations of these principles in a single algorithm is studied. Expand
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