Sūn-hong-hiòng sîn-keng bāng-lō͘

機械學習佮 數據滑挖
問題 分類 聚類 迴歸 異常檢測 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

順風向 (Sūn-hong-hiòng)神經 (sîn-keng)網路 (bāng-lō͘) (英語 (Eng-gí): feedforward neural network) 是 (sī)一 (chi̍t)款 (khoán)內部 (lāi-pō͘)無 (bô)循環 (sûn-khoân)連結 (liân-kiat)的 (ê)人工 (jîn-kang)神經網路。順風向神經網路是上 (siāng)早 (chá)出範 (chhut-hoān)嘛 (mā)上間單 (kan-tan)的人工神經網路。