机器学习算法分类
机器学习算法
范围
计算机视觉
语音识别
自然语言处理
主题模型
潜在语义分析(pLSA)
LDA隐含狄利克雷分析
标注
HMM
CRF
模式识别
统计学习
数据挖掘
关联规则
Apriori
Eclat
FP-growth
算法
监督学习(supervised)
回归
线性回归(Linear Regression)
简单线性回归
多元线性回归
非线性回归(nonlinear Regression)
Generalized additive models
多元自适应回归样条(multivariate adaptive regreesion splines,MARS)
hierarchical mixtures of experts(HME)
patient rule induction method(PRIM)
CART
分类
Logic based algorithms
rule-based classifiers
RIPPER
决策树(decision tree)
ID3(Iterative Dichotomiser3)
C4.5
C5.0
分类及回归树(Classification And Regreession Tree,CART)
卡方自动交互检测(Chi-squared Automatic Interaction Detection,CHAID)
M5(模型树)
决策树桩(Decision Stump,单层决策树)
Statistical learning algorithms
贝叶斯算法
朴素贝叶斯(Naive Bayes,NB)
先验概率
后验概率
高斯朴素贝叶斯(Gaussian Naive Bayes)
平均单依赖估计(Averaged One-Dependence Estimators,AODE)
贝叶斯网络(Bayesian Network,BN)
参数未知(进行条件概率估算)
梯度训练算法
EM算法
结构未知(通过已知数据启发式学习网络结构)
K2算法
Instance-based learning algorithms
K最邻近节点算法(k-Nearest Neighbor algorithm,KNN)
支持向量机(Support Vector Machine,SVM)
逻辑回归(Logistic Regression)
二分类
多分类
Softmax回归模型
集成算法(Ensemble algorithms)
Bagging(Bootstrap aggregating)
随机森林(Randomized trees)
Boosting
AdaBoost
Gradient Boosting Machine
Stacking
无监督学习(unsupervised)
聚类算法(clustering)
层次聚类(Hierarchical clustering)
BIRCH
CURE
ROCK
Chameleon
基于平方误差的聚类(矢量量化)Squared Error—Based Clustering,Vector Quantization)
K-means
PAM(partitioning around medoids/K-medoids)
ISODATA( iterative self-organizing data analysis technique)
基于混合密度的聚类(Mixture Densities-Based Clustering )
DBSCAN
DENCLUE
OPTICS
GMDD(Gaussian mixture density decomposition)
基于网格的聚类(Grid-based Clustering)
STING
CLIQUE
WaveCluster
基于图论的聚类(Graph Theory-Based Clustering)
CLICK
CAST
HCS(Highly connected subgrahs)
DTG(Delaunay triangulation graph )
模糊聚类(Fuzzy Clustering)
FCM
MM(mountain method)
PCM
FCS
基于组合搜索技术的聚类(Combinatorial Search Techniques-Based Clustering)
TS clustering
SA clustering
GGA(Genetically guided algorithm)
核聚类(Kernel-Based Clustering)
Kernel K-means
SVC(support vector clustering)
基于神经网络的聚类(Neural Networks-Based Clustering)
学习矢量量化(Learning Vector Quantization,LVQ)
自组织映射网(Self-Organizing Feature Map,SOFM)
自适应共振理论(adaptive resonance theory ,ART)
降维算法(dimension reduce)
因子分析(Factor Analysis)
主成份分析(Principle Component Analysis,PCA)
独立成分分析 (Independent Component Analysis, ICA)
多维尺度(Multi-Dimensional Scaling,MDS)
投影追踪(Projection Pursuit)
LLE局部线性嵌入
SVD
LDA线性判别分析
偏最小二乘回归(Partial Least Square Regression,PLS)
Sammon映射
半监督学习
self-Training(自训练)
Transductive Learning(直推式学习)
Generative Model(生成式模型)
Co-Training (协同训练)
Multiview Learning(多视角学习)
Graph-Based Model(图模型)
强化学习
VI Algorithm(VI 算法)
PI Algorithm(PI算法)
Robbins-Monro algorithm(Robbins-Monro算法)
Q-P-Learning Algorithm(Q-P-Learning算法)
Actor-Critic Algorithm(Actor-Critic算法)
Q-Learning Algorithm(Q-Learning算法)
SARSA Algorithm(SARSA算法)
深度学习
Deep Boltzmann Machine(深度玻尔兹曼机)
Deep Belief Networks(深度信念网络)
Convolutional Neural Network(卷积神经网络)
Stacked Auto-encoders(栈式自编码)
Perceptron Neural Network(感知机神经网络)
Back Propagation Algorithm(向后传播算法)
Recurrent neural networks(循环神经网络)
application:computer visional
application:nature language processing
应用
时序挖掘
分类
聚类
轨迹挖掘
关系挖掘
回归