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k. -nearest neighbors algorithm. In statistics, the k-nearest neighbors algorithm ( k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, [1] and later expanded by Thomas Cover. [2] It is used for classification and regression. In both cases, the input consists of the k closest training ...
Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g. step-functions in decision trees instead of continuous function in a linear regression model ). Learning is the process of apprehending useful knowledge by observing and interacting with the world. [2]
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order. Recursive neural networks, sometimes abbreviated ...
Bias and variance as function of model complexity. In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number ...
Similarity learning. Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the goal is to learn a similarity function that measures how similar or related two objects are. It has applications in ranking, in recommendation systems, visual identity ...
Vapnik–Chervonenkis dimension. In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the size (capacity, complexity, expressive power, richness, or flexibility) of a class of sets. The notion can be extended to classes of binary functions. It is defined as the cardinality of the largest set of points that ...
The associative neural network (ASNN) is an extension of committee of machines that combines multiple feedforward neural networks and the k-nearest neighbor technique. It uses the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the Bias of the neural network ensemble.
Structured kNN. Structured k-Nearest Neighbours [1] [2] [3] is a machine learning algorithm that generalizes the k-Nearest Neighbors (kNN) classifier. Whereas the kNN classifier supports binary classification, multiclass classification and regression, [4] the Structured kNN (SkNN) allows training of a classifier for general structured output ...