Sūn-hong-hiòng sîn-keng bāng-lō͘

Ki-hāi ha̍k-si̍p kap sò͘-kì ku̍t-óe
Būn-tê Hun-lūi Chū-lūi Hôe-kui Ī-siông kiám-chhek Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction
Supervised learning (classification • regression) Decision trees Ensembles (Bagging, Boosting, Random forest) k-NN Linear regression Naive Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Expectation–maximization (EM) DBSCAN OPTICS Mean-shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA t-SNE
Structured prediction Graphical models (Bayes net, CRF, HMM)
Anomaly detection k-NN Local outlier factor
Neural nets Autoencoder Deep learning Multilayer perceptron RNN Restricted Boltzmann machine SOM Convolutional neural network
Reinforcement learning Q-learning SARSA Temporal difference (TD)
Theory Bias-variance dilemma Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues NIPS ICML ML JMLR ArXiv:cs.LG
Related articles List of datasets for machine-learning research Outline of machine learning

Sūn-hong-hiòng sîn-keng bāng-lō͘ (Eng-gí: feedforward neural network) sī chi̍t khoán lāi-pō͘ bô sûn-khoân liân-kiat ê jîn-kang sîn-keng bāng-lō͘. Sūn-hong-hiòng sîn-keng bāng-lō͘ sī siāng chá chhut-hoān mā siāng kan-tan ê jîn-kang sîn-keng bāng-lō͘.