ML - home

Home 성능평가 전처리	Classification	Clustering	Association	Numerical  Prediction
Rule	Zero-R, One-R, PART, Ripper(JRip),
Regression	OLSR, Logistic, Stepwise, MARS, LOESS, SVM			Linear,
Regularization	Ridge(L2 penalty), LASSO(L1 penalty), Elastic Net, LARS,
Bayesian	Naive, Gaussian Naive, Multinominal, AODE, BBN, BN,
Decision Tree	CART, ID3, C4.5, C5.0, CHAID, Decision Stump, M5, Conditional Decision, Tree,
Instance Based	KNN(IBK), LVQ, SOM, LWL,
Clustering		k-Means, k-Medians, Hierarchical, EM,
Association Rule Learning			Apriori, Eclat,
Artificial Neural Network	Perceptron, MLP, Hopfield Network, RBFN,
Deep Learning	DBM, DBN, CNN, Stacked Auto-Encoder.
Ensemble	Boosting, Bagging, AdaBoost, Blending, GBM, GBRT, Random Forest
Ect	Computational Intelligence, Computer Vision(CV), Natural Language Processing(NLP), Recommender Systems, Reinforcement Learning, Graphical Model

https://medium.com/analytics-vidhya

https://tensorflow.blog/

https://jonathan-hui.medium.com/  -> ㄹㅇ 봐야함

위는 내가 볼 사이트 메모

용어정리

Artificial intelligence = 포괄적인 의미의 인공지능. 꼭 컴터가 아니어도 됨.

Machine Learning = 데이터를 기반으로 일반화 패턴을 찾는 알고리즘. 

Deep Learning = 신공망을 기초로 한 인공지능. 이걸로 이제 비정형 데이터도 쉽게 가능.

Data Science = data 를 information 으로 가공하는 일

Big Data = data 가 매우 많음

Model = data 를 일반화해서 얻은 알고리즘의 instance. 모델을 잘 찾는게 목적임.

Feature = data table 의 column 처럼 data 의 변수. Attributes 라고도 함.

Class = classification 에서의 분류값. Label 이라고도 함.

기계학습 분류

Classification

data set 의 features 사이에 class 를 결정하는 decision boundary 를 만드는 것

다른말로 하면 hyperplane of feature space 를 찾는 것

Clustering

Association

Numerical Prediction

Algorithms 종류

1. Regression Algorithms

Ordinary Least Squares Regression(OLSR)

Linear Regression

Logistic Regression

Stepwise Regression

Multivariate Adaptive Regression Splines (MARS)

Locally Estimated Scatterplot Smoothing (LOESS)

2. Instance Based Algorithms(IBK)

K-Nearest Neighbors (KNN, IBK)

Learning Vector Quantization (LVQ)

Self-Organizing Map (SOM)

Locally Weighted Learning (LWL)

3. Regularization Algorithms

Ridge Regression

Least Absolute Shrinkage and Selection Operator (LASSO)

Elastic Net

Least-Angle Regression (LARS)

4. Decision Tree Algorithms

Classification and Regression Tree (CART)

Iterative Dichotomiser 3 (ID3)

C4.5 and C5.0

Chi-squared Automatic Interaction Detection(CHAID)

Decision Stump

M5

Conditional Decision Trees

5. Bayesian Algorithms

Naive Bayes

Gaussian Naive Bayes

Multinomial Naive Bayes

Averaged One-Dependence Estimators (AODE)

Bayesian Belief Network (BBN)

Bayesian Network (BN)

6. Clustering Algorithms

k-Means

k-Medians

Expectation Maximization (EM)

Hierarchical Clustering 

7. Association Rule Learning Algorithms

Apriori Algorithms

Eclat Algorithms

8. Artificial Neural Network Algorithms

Perceptron

Back-Propagation

Hopfield Network

Radial Basis Function Network (RBFN)

9. Deep Learning 

Deep Boltzmann Machine(DBM)

Deep Belief Network (DBN)

Convolutional Neural Network(CNN)

Stacked Auto Encoders

10. Dimensionality Reduction

Principal Component Analysis (PCA)

Principal Component Regression (PCR)

Partial Least Squares Regression (PLSR)

Sammon Mapping

Multidimensional Scaling (MDS)

Projection Pursuit

Linear Discriminant Analysis (LDA)

Mixture Discriminant Analysis (MDA)

Quadratic Discriminant Analysis (QDA)

Flexible Discriminant Analysis (FDA)

11. Ensemble Algorithms

Boosting

Bootstrapped Aggregation (Bagging)

AdaBoost

Stacked Generalization (blending)

Gradient Boosting Machine (GBM)

Gradient Boosted Regression Tree(GBRT)

Random Forest

12. Other

Computational Intelligence (evolutionary algorithms)

Computer Vision (CV)

Natural Language Processing (NLP)

Recommender Systems

Reinforcement Leanring

Graphical Models