Transductive Semi-Supervised Learning Algorithm
A Transductive Semi-Supervised Learning Algorithm is a semi-supervised leaning algorithm that only needs to predictions on the unlabeled records.
- AKA: Transductive Learning Algorithm.
- Context:
- Example(s):
- Counter-Example(s):
- an Inductive Semi-Supervised Learning Algorithm, that will be tested on Unseen Unlabeled Records.
- A Fully-Supervised Learning Algorithm.
- A Unsupervised Learning Algorithm.
- See: Metric-based Learning.
References
2006
- (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.
1999
- (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.