Machine Learning

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24 domain-specific abstractions whose origin domain is Machine Learning.

• Attention (machine learning) — A neural-network mechanism that computes context-dependent weights over representations and combines them so each output can focus selectively on relevant inputs.
• Boosting — A supervised ensemble procedure that builds predictors sequentially so each new learner corrects or reweights errors left by the current aggregate.
• Decision tree pruning — The removal or replacement of low-value branches from a decision tree to reduce complexity and improve expected generalization.
• Decision-theoretic rough sets — A probabilistic rough-set framework that derives lower, boundary and negative decision regions by minimizing expected loss under conditional class probabilities.
• Deep belief network — A multilayer generative model whose top layers form an undirected associative model and lower layers form directed latent-variable connections.
• Deep learning — Learn task-relevant hierarchical representations with multilayer parameterized neural networks trained end to end by optimization over data, enabling complex prediction and generation at the cost of opacity, data dependence, and compute.
• Ensemble learning — A machine-learning strategy combining predictions from multiple models so their complementary errors yield a stronger aggregate predictor.
• Evolutionary acquisition of neural topologies — An evolutionary reinforcement-learning method that jointly evolves artificial-neural-network structure and weights, using structural mutation and evolution-strategy parameter optimization.
• Generative adversarial network — A generative-learning framework in which a generator and discriminator are trained in opposition so generated samples approach a data distribution.
• Graph neural network — A neural-network family that learns node, edge or whole-graph representations by repeatedly aggregating relational information in a permutation-aware graph structure.
• Hidden layer — A neural-network layer situated between inputs and outputs whose learned nonlinear transformations construct intermediate representations used by later layers.
• Hybrid Kohonen self-organizing map — A neural architecture coupling a self-organizing map front end to supervised hidden and output layers.
• Kernel principal component analysis — Nonlinear dimensionality reduction obtained by performing PCA in an implicit reproducing-kernel feature space.
• Lazy learning — A machine-learning strategy that postpones generalization from stored training examples until a prediction query arrives.
• Logistic model tree — Partition predictor space with a decision tree while fitting logistic-regression models through the tree, yielding piecewise probabilistic classification whose local linear logits are induced, inherited, and pruned together.
• Machine-Learning Learning Curve — Compare training and validation performance across increasing data or optimizer progress so curve levels, gaps, and slopes diagnose what is limiting a model and what intervention is likely to help.
• Multiple instance learning — A supervised-learning setting in which labels attach to bags of instances while instance-level labels are absent or only indirectly constrained.
• Neural scaling law — An empirical relation describing how neural-network loss or capability changes with model size, data, training compute or inference compute.
• Neural Turing machine — A differentiable recurrent architecture coupling a neural controller to an addressable external memory.
• Out-of-bag error — A predictive-error estimate computed for each training case using only bagged models that excluded it from their bootstrap samples.
• Self-organizing map — Train prototype vectors arranged on a low-dimensional lattice by repeatedly moving a best-matching unit and its lattice neighbors toward input samples, producing a topology-oriented representation of high-dimensional data.
• Swish function — A smooth neural-network activation family fβ(x)=x·sigmoid(βx) that interpolates between a scaled linear map and a ReLU-like gate while remaining mildly nonmonotonic for positive β.
• Transfer learning — A machine-learning strategy that reuses representations, parameters or examples learned in a source task or domain to improve learning in a related target task.
• Zero-shot learning — A learning setup that predicts classes absent from training by transferring through auxiliary semantic descriptions or attributes shared with seen classes.