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Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Identifying the in-place algorithms with L has some interesting implications; for example, it means that there is a (rather complex) in-place algorithm to determine whether a path exists between two nodes in an undirected graph, [3] a problem that requires O(n) extra space using typical algorithms such as depth-first search (a visited bit for ...
The performance of machine learning algorithms is commonly visualized by learning curve plots that show estimates of the ... For leave-one-out stability in ...
A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. For instance, consider a machine learning algorithm that is being trained to recognize handwritten letters of the alphabet, using 1000 examples of handwritten letters and their labels ("A" to "Z") as a training set. One ...
Wolpert had previously derived no free lunch theorems for machine learning (statistical inference). [2] In 2005, Wolpert and Macready themselves indicated that the first theorem in their paper "state[s] that any two optimization algorithms are equivalent when their performance is averaged across all possible problems". [3]
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Each search algorithm performs well on almost all objective functions. [11] So if one is not concerned with the "relatively small" differences between search algorithms, e.g., because computer time is cheap, then you shouldn't worry about no free lunch. An algorithm may outperform another on a problem when neither is specialized to the problem.
Supervised learning; Unsupervised learning; Semi-supervised learning; Self-supervised learning; Reinforcement learning; Meta-learning; Online learning; Batch learning; Curriculum learning; Rule-based learning; Neuro-symbolic AI; Neuromorphic engineering; Quantum machine learning