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The most well-known example of a case-bases learning algorithm is the k-nearest neighbor algorithm, which is related to transductive learning algorithms. [2] Another example of an algorithm in this category is the Transductive Support Vector Machine (TSVM). A third possible motivation of transduction arises through the need to approximate.
The transductive learning framework was formally introduced by Vladimir Vapnik in the 1970s. [6] Interest in inductive learning using generative models also began in the 1970s. A probably approximately correct learning bound for semi-supervised learning of a Gaussian mixture was demonstrated by Ratsaby and Venkatesh in 1995. [7]
Transduction (machine learning), the process of directly drawing conclusions about new data from previous data, without constructing a model; Transduction (physiology), the transportation of stimuli to the nervous system; Transduction (psychology), reasoning from specific cases to general cases, typically employed by children during their ...
Transductive support vector machines extend SVMs in that they could also treat partially labeled data in semi-supervised learning by following the principles of transduction. Here, in addition to the training set D {\displaystyle {\mathcal {D}}} , the learner is also given a set
Transductive reasoning is when a child fails to understand the true relationships between cause and effect. [ 39 ] [ 44 ] Unlike deductive or inductive reasoning (general to specific, or specific to general), transductive reasoning refers to when a child reasons from specific to specific, drawing a relationship between two separate events that ...
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Conformal prediction (CP) is a machine learning framework for uncertainty quantification that produces statistically valid prediction regions (prediction intervals) for any underlying point predictor (whether statistical, machine, or deep learning) only assuming exchangeability of the data. CP works by computing nonconformity scores on ...
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