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ML & DL

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Active Learning

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Core-set Selection

Diversity-based Sampling

Expected Error Reduction

Expected Model Change

Query-by-Committee

Uncertainty Sampling

Imitation Learning

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Behavior Cloning

DAgger

Generative Adversarial Imitation Learning (GAIL)

Inverse Reinforcement Learning (IRL)

Maximum Entropy IRL

Reinforcement Learning

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Bandit

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ε-Greedy

Multi-Armed Bandit

Thompson Sampling

Upper Confidence Bound (UCB)

Deep Reinforcement Learning

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Deep Q-Network (DQN)

MuZero

Proximal Policy Optimization (PPO)

Soft Actor-Critic (SAC)

Dynamic Programming

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Policy Evaluation

Policy Iteration

Value Iteration

关键对象

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Action

Environment Model

Policy

Reward

State

Value Function

Monte Carlo RL

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Monte Carlo Control

Monte Carlo Prediction

Temporal Difference Learning

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Q-Learning

SARSA

TD(0)

Self-supervised Learning

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自回归预测

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GPT-style Next-token Prediction

n-gram Language Model

RNN Language Model

Transformer Language Model

对比学习

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Barlow Twins

BYOL

CLIP-style Image-Text Contrastive Learning

MoCo

SimCLRbi

SwAV

Word2Vec

去噪学习

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BART-style Denoising

Denoising Autoencoder

Diffusion Denoising Objective

掩码预测

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BERT-style Pretraining

Masked Autoencoder (MAE)

Masked Image Modeling

Masked Language Modeling (MLM)

预测式表示学习

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Bootstrap Representation Learning

Contrastive Predictive Coding (CPC)

Semi-supervised Learning

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Consistency Regularization

FixMatch

Pseudo-labeling

Supervised Learning

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分类模型

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AdaBoost

CNN Classifier

Decision Tree

k-Nearest Neighbors (kNN)

Logistic Regression

Multilayer Perceptron (MLP)

Naive Bayes

Random Forest

RNN Classifier

Softmax Regression

Support Vector Machine (SVM)

Transformer Classifier

回归模型

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Elastic Net

Gaussian Process Regression

Gradient Boosting Regression

Lasso Regression

Linear Regression

Neural Network Regression

Random Forest Regression

Ridge Regression

Support Vector Regression (SVR)

Unsupervised Learning

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聚类方法

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Gaussian Mixture Model (GMM；常用 EM 算法训练)

K-Means

Spectral Clustering

密度估计

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Gaussian Mixture Model (GMM)

Kernel Density Estimation (KDE)

降维方法

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Principal Component Analysis (PCA)

异常检测

ML & DL Resources

本页目录

Principal Component Analysis (PCA)

本页用于记录 Principal Component Analysis (PCA) 的定义、基本思想、适用场景和相关链接。

所属路径

Unsupervised Learning / 降维方法 / Principal Component Analysis (PCA)

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