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The meaning of some terms in DM

Basis(基础):

MSE(Mean Square Error 均方误差),

LMS(LeastMean Square 最小均方),

LSM(Least Square Methods 最小二乘法),

MLE(MaximumLikelihood Estimation最大似然估计),

QP(Quadratic Programming 二次规划),

CP(Conditional Probability条件概率),

JP(Joint Probability 联合概率),

MP(Marginal Probability边缘概率),

Bayesian Formula(贝叶斯公式),

L1 /L2Regularization(L1/L2正则,以及更多的,现在比较火的L2.5正则等),

GD(GradientDescent 梯度下降),

SGD(Stochastic Gradient Descent 随机梯度下降),

Eigenvalue(特征值),

Eigenvector(特征向量),

QR-decomposition(QR分解),

Quantile (分位数),

Covariance(协方差矩阵)。

Common Distribution(常见分布):

Discrete Distribution(离散型分布):

BernoulliDistribution/Binomial(贝努利分布/二项分布),

Negative BinomialDistribution(负二项分布),

MultinomialDistribution(多项式分布),

Geometric Distribution(几何分布),

HypergeometricDistribution(超几何分布),

Poisson Distribution (泊松分布)。

Continuous Distribution (连续型分布):

UniformDistribution(均匀分布),

Normal Distribution /Guassian Distribution(正态分布/高斯分布),

ExponentialDistribution(指数分布),

Lognormal Distribution(对数正态分布),

GammaDistribution(Gamma分布),

Beta Distribution(Beta分布),

Dirichlet Distribution(狄利克雷分布),

Rayleigh Distribution(瑞利分布),

Cauchy Distribution(柯西分布),

Weibull Distribution (韦伯分布)。

Three Sampling Distribution(三大抽样分布):

Chi-squareDistribution(卡方分布),

t-distribution(t-distribution),

F-distribution(F-分布)。

Data Pre-processing(数据预处理):

Missing Value Imputation(缺失值填充),

Discretization(离散化),Mapping(映射),

Normalization(归一化/标准化)。

Sampling(采样):

Simple Random Sampling(简单随机采样),

OfflineSampling(离线等可能K采样),

Online Sampling(在线等可能K采样),

Ratio-based Sampling(等比例随机采样),

Acceptance-RejectionSampling(接受-拒绝采样),

Importance Sampling(重要性采样),

MCMC(MarkovChain Monte Carlo 马尔科夫蒙特卡罗采样算法:Metropolis-Hasting& Gibbs)。

Clustering(聚类):

K-Means,

K-Mediods,

二分K-Means,

FK-Means,

Canopy,

Spectral-KMeans(谱聚类),

GMM-EM(混合高斯模型-期望最大化算法解决),

K-Pototypes,CLARANS(基于划分),

BIRCH(基于层次),

CURE(基于层次),

DBSCAN(基于密度),

CLIQUE(基于密度和基于网格)。

Classification&Regression(分类&回归):

LR(Linear Regression 线性回归),

LR(LogisticRegression逻辑回归),

SR(Softmax Regression 多分类逻辑回归),

GLM(GeneralizedLinear Model 广义线性模型),

RR(Ridge Regression 岭回归/L2正则最小二乘回归),

LASSO(Least Absolute Shrinkage andSelectionator Operator L1正则最小二乘回归),

RF(随机森林),

DT(DecisionTree决策树),

GBDT(Gradient BoostingDecision Tree 梯度下降决策树),

CART(ClassificationAnd Regression Tree 分类回归树),

KNN(K-Nearest Neighbor K近邻),

SVM(Support VectorMachine),

KF(KernelFunction 核函数PolynomialKernel Function 多项式核函、

Guassian KernelFunction 高斯核函数/Radial BasisFunction RBF径向基函数、

String KernelFunction 字符串核函数)、

NB(Naive Bayes 朴素贝叶斯),

BN(Bayesian Network/Bayesian Belief Network/ Belief Network 贝叶斯网络/贝叶斯信度网络/信念网络),

LDA(Linear Discriminant Analysis/FisherLinear Discriminant 线性判别分析/Fisher线性判别),

EL(Ensemble Learning集成学习Boosting,Bagging,Stacking),

AdaBoost(Adaptive Boosting 自适应增强),

MEM(MaximumEntropy Model最大熵模型)。

Effectiveness Evaluation(分类效果评估):

Confusion Matrix(混淆矩阵),

Precision(精确度),

Recall(召回率),

Accuracy(准确率),

F-score(F得分),

ROC Curve(ROC曲线),

AUC(AUC面积),

LiftCurve(Lift曲线) ,

KS Curve(KS曲线)。

PGM(Probabilistic Graphical Models概率图模型):

BN(Bayesian Network/Bayesian Belief Network/ BeliefNetwork 贝叶斯网络/贝叶斯信度网络/信念网络),

MC(Markov Chain 马尔科夫链),

HMM(HiddenMarkov Model 马尔科夫模型),

MEMM(Maximum Entropy Markov Model 最大熵马尔科夫模型),

CRF(ConditionalRandom Field 条件随机场),

MRF(MarkovRandom Field 马尔科夫随机场)。

NN(Neural Network神经网络):

ANN(Artificial Neural Network 人工神经网络),

BP(Error BackPropagation 误差反向传播)。

Deep Learning(深度学习):

Auto-encoder(自动编码器),

SAE(Stacked Auto-encoders堆叠自动编码器,

Sparse Auto-encoders稀疏自动编码器、

Denoising Auto-encoders去噪自动编码器、

Contractive Auto-encoders 收缩自动编码器),

RBM(RestrictedBoltzmann Machine 受限玻尔兹曼机),

DBN(Deep Belief Network 深度信念网络),

CNN(ConvolutionalNeural Network 卷积神经网络),

Word2Vec(词向量学习模型)。

DimensionalityReduction(降维):

LDA LinearDiscriminant Analysis/Fisher Linear Discriminant 线性判别分析/Fisher线性判别,

PCA(Principal Component Analysis 主成分分析),

ICA(IndependentComponent Analysis 独立成分分析),

SVD(Singular Value Decomposition 奇异值分解),

FA(FactorAnalysis 因子分析法)。

Text Mining(文本挖掘):

VSM(Vector Space Model向量空间模型),

Word2Vec(词向量学习模型),

TF(Term Frequency词频),

TF-IDF(Term Frequency-Inverse DocumentFrequency 词频-逆向文档频率),

MI(MutualInformation 互信息),

ECE(Expected Cross Entropy 期望交叉熵),

QEMI(二次信息熵),

IG(InformationGain 信息增益),

IGR(Information Gain Ratio 信息增益率),

Gini(基尼系数),

x2 Statistic(x2统计量),

TEW(TextEvidence Weight文本证据权),

OR(Odds Ratio 优势率),

N-Gram Model,

LSA(Latent Semantic Analysis 潜在语义分析),

PLSA(ProbabilisticLatent Semantic Analysis 基于概率的潜在语义分析),

LDA(Latent DirichletAllocation 潜在狄利克雷模型)。

Association Mining(关联挖掘):

Apriori,

FP-growth(Frequency Pattern Tree Growth 频繁模式树生长算法),

AprioriAll,

Spade。

Recommendation Engine(推荐引擎):

DBR(Demographic-based Recommendation 基于人口统计学的推荐),

CBR(Context-basedRecommendation 基于内容的推荐),

CF(Collaborative Filtering协同过滤),

UCF(User-basedCollaborative Filtering Recommendation 基于用户的协同过滤推荐),

ICF(Item-basedCollaborative Filtering Recommendation 基于项目的协同过滤推荐)。

Similarity Measure&Distance Measure(相似性与距离度量):

Euclidean Distance(欧式距离),

ManhattanDistance(曼哈顿距离),

Chebyshev Distance(切比雪夫距离),

MinkowskiDistance(闵可夫斯基距离),

Standardized Euclidean Distance(标准化欧氏距离),

MahalanobisDistance(马氏距离),

Cos(Cosine 余弦),

HammingDistance/Edit Distance(汉明距离/编辑距离),

JaccardDistance(杰卡德距离),

Correlation Coefficient Distance(相关系数距离),

InformationEntropy(信息熵),

KL(Kullback-Leibler Divergence KL散度/Relative Entropy 相对熵)。

Optimization(最优化):

Non-constrainedOptimization(无约束优化):

Cyclic VariableMethods(变量轮换法),

Pattern Search Methods(模式搜索法),

VariableSimplex Methods(可变单纯形法),

Gradient Descent Methods(梯度下降法),

Newton Methods(牛顿法),

Quasi-NewtonMethods(拟牛顿法),

Conjugate Gradient Methods(共轭梯度法)。

ConstrainedOptimization(有约束优化):

Approximation Programming Methods(近似规划法),

FeasibleDirection Methods(可行方向法),

Penalty Function Methods(罚函数法),

Multiplier Methods(乘子法)。

Heuristic Algorithm(启发式算法),

SA(SimulatedAnnealing,模拟退火算法),

GA(genetic algorithm遗传算法)。

Feature Selection(特征选择算法):

Mutual Information(互信息),

DocumentFrequence(文档频率),

Information Gain(信息增益),

Chi-squared Test(卡方检验),

Gini(基尼系数)。

Outlier Detection(异常点检测算法):

Statistic-based(基于统计),

Distance-based(基于距离),

Density-based(基于密度),

Clustering-based(基于聚类)。

Learning to Rank(基于学习的排序):

Pointwise:McRank;

Pairwise:RankingSVM,RankNet,Frank,RankBoost;

Listwise:AdaRank,SoftRank,LamdaMART。

source: http://blog.csdn.net/heyongluoyao8/article/details/39299913