Semi-Supervised Learning Algorithm
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A semi-supervised leaning algorithm is a supervised learning algorithm that can be applied by a semi-supervised learning system (to solve a semi-supervised training task - with unlabeled training data).
- It can range from being a Transductive Semi-Supervised Learning Algorithm to being a Inductive Semi-Supervised Learning Algorithm, depending on whether the test set is visible during learning.
- It can range from being a Semi-Supervised Classification Algorithm to being a Semi-Supervised Point Estimation Algorithm.
- It can be a Self-Supervised Learning Algorithm, when a Labeling Heuristic is available.
- It can be a Co-Training Learning Algorithm, if ???;
- It can be a Weakly-Trained Learning Algorithm (if small training set) to being a Large Labeled Dataset Semi-Supervised Algorithm.
- See: Metric-based Learning.
- (Zhu et al., 2009) ⇒ Xiaojin Zhu, and Andrew B. Goldberg. (2009). “Introduction to Semi-Supervised Learning.” In: Morgan and Claypool Publishers. ISBN: 1598295470.
- (Zhu, 2008) ⇒ Xiaojin Zhu. (2008). “Semi-Supervised Learning Literature Survey (revised edition)." Technical Report 1530, Department of Computer Sciences, University of Wisconsin, Madison.
- (Zhu, 2007) ⇒ Xiaojin Zhu. (2007). “Semi-Supervised Learning." Tutorial at ICML 2007.
- (Chapelle et al., 2006a) ⇒ Olivier Chapelle (editor), Alexander Zien (editor), and Bernhard Schölkopf (editor). (2006). “Semi-Supervised Learning.” MIT Press. ISBN:0262033585
- (Chapelle et al., 2006b) ⇒ Olivier Chapelle, Alexander Zien, and Bernhard Schölkopf. (2006). “Introduction to Semi-Supervised Learning.” In: (Chapelle et al., 2006a)
- (Chapelle et al., 2006b) ⇒ Olivier Chapelle, Alexander Zien, and Bernhard Schölkopf (Editors). (2006). “Introduction to Semi-Supervised Learning.” In: (Chapelle et al., 2006a)
- QUOTE: A problem related to SSL was introduced by Vapnik already several decades ago: so-called transductive learning. In this setting, one is given a (labeled) training set and an (unlabeled) test set. The idea of transduction is to perform predictions only for the test points. This is in contrast to inductive learning, where the goal is to output a prediction function which is defined on the entire space X. Many methods described in this book will be transductive; in particular, this is rather natural for inference based on graph representations of the data. This issue will be addressed again in section 1.2.4.
- (Zhu, 2005) ⇒ Xiaojin Zhu. (2005). “Semi-supervised learning literature survey." Technical Report TR-1530. University of Wisconsin-Madison Department of Computer Science.
- (Basu et al., 2004) ⇒ Sugato Basu, Mikhail Bilenko, and Raymond Mooney. (2004). “A Probabilistic Framework for Semi-Supervised Clustering.” In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004). doi:10.1145/1014052.1014062
- (Seeger, 2001) ⇒ Matthias Seeger. (2001). “Learning with Labeled and Unlabeled Data." Technical Report. University of Edinburgh.
- (Joachims, 1999) ⇒ Thorsten Joachims. (1999). “Transductive Inference for Text Classification using Support Vector Machines.” In: Proceedings of the International Conference on Machine Learning (ICML 1999).
- QUOTE: The work presented here tackles the problem of learning from small training samples by taking a transductive (Vapnik, 1998), instead of an inductive approach. In the inductive setting the learner tries to induce a decision function which has a low error rate on the whole distribution of examples for the particular learning task. Often, this setting is unnecessarily complex. In many situations we do not care about the particular decision function, but rather that we classify a given set of examples (i.e. a test set) with as few errors as possible. This is the goal of transductive inference. Some examples of transductive text classification tasks are the following. All have in common that there is little training data, but a very large test set.