Chhim-tō͘ ha̍k-si̍p

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

深度 (Chhim-tō͘)學習 (ha̍k-si̍p) (英語 (Eng-gí): deep learning) 是 (sī)機械 (ki-hāi)學習的 (ê)一 (chi̍t)个 (ê)分野 (hun-iá)。這 (Che)是一種 (chióng)試圖 (chhì-tô͘)欲 (beh)使用 (sú-iōng)包含 (pau-hâm)複雜 (ho̍k-cha̍p)結構 (kiat-kò͘)抑是 (ia̍h-sī)由 (iû)濟 (chē)重 (têng)非線性 (hui-sòaⁿ-sèng)變換 (piàn-ōaⁿ)構成 (kò͘-sêng)的濟的處理層 (chhú-lí-chân)對 (tùi)資料 (chu-liāu)進行 (chìn-hêng)高層 (ko-chân)抽象 (thiu-siōng)的演算法 (ián-sǹg-hoat)。學習的過程 (kòe-têng)會當 (ē-tàng)是監督 (kàm-tok)，半監督 (pòaⁿ-kàm-tok)抑是無監督 (bô-kàm-tok)學習。

深度學習是機械學習內 (lāi)一種對資料做 (chò)特徵 (te̍k-teng)學習的演算法。