MindMap Gallery Machine learning algorithm classification
Summary of classification of machine learning algorithms! The figure below summarizes the classification of machine learning algorithms, including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, transfer learning, deep learning algorithms, etc. Hope this picture can help you!
Edited at 2020-04-08 10:06:19This is a panoramic infographic—currently sweeping across the web—illustrating the comprehensive applications of OpenClaw, a popular open-source AI agent platform. It systematically introduces this intelligent agent framework—affectionately dubbed "Lobster Farming"—helping readers quickly grasp its core value, technical features, application scenarios, and security protocols. It serves as an excellent introductory guide and practical manual.
這是一張最近風靡全網關於熱門開源AI代理平台OpenClaw的全網應用全景圖解。它系統性地介紹了這款被稱為「養龍蝦」的智慧體框架,幫助讀者快速理解其核心價值、技術特性、應用場景及安全規範,是一份極佳的入門指南與實操手冊。此圖主要針對希望利用AI建構自動化工作流程的技術從業人員、中小企業主及效率追求者,透過9大模組層層遞進,全面剖析了OpenClaw從概念到落地的整個過程。 圖中核心內容首先釐清了「養龍蝦」指涉的是OpenClawd開源智能體,並強調其本質是「AI基建」而非一般聊天機器人。隨後詳細比較其與傳統AI助理的區別,擁有記憶管理、權限控制、會話隔離和異常恢復四大基礎能力,支援跨平台存取和多模型相容(如GPT、Claude、Ollama)。同時,圖解提供了完整的部署方案(雲端/本地/Docker),並列舉了辦公室自動化、內容創作、資料收集等五大應用程式場景。此外,還展示了其火爆程度、政府與大廠佈局、安全部署建議及適合/不適合的人群分類。幫助你快速掌握OpenClaw技術架構與應用價值,指導個人或企業建構AI自動化系統,規避資料外洩與權限失控風險,是學習「執行式AI」轉型的權威參考圖譜。
本圖由萬興腦圖繪製,是針對IT研發崗位的結構化個人履歷模板,完整涵蓋求職核心資訊模組。基本資訊區包含姓名、電話、信箱、求職意願及GitHub連結;專業概要要求以2-3句提煉核心優勢;工作經驗以「公司A高級Java開發工程師」為例,以「透過(行動),達成(量化成果)」格式呈現微服務架構設計、系統效能優化、團隊技術規範制定等職責,公司B經歷則聚焦功能模組開發與Elasticsearch搜尋優化;技能專長分程式語言、後端框架、中介軟體、資料庫、容器雲等維度,清楚展示技術堆疊;專案成果以「電商平台秒殺系統」為例,說明技術棧、架構設計、個人貢獻(Redis Lua庫存原子扣減)及KPI;教育背景包含一流大學電腦專業學歷,以及AWS認證解決方案架構師、軟考中級軟體設計師證書。模板邏輯嚴謹,涵蓋IT研發求職全流程關鍵訊息,幫助求職者清晰、量化展示專業能力。
This is a panoramic infographic—currently sweeping across the web—illustrating the comprehensive applications of OpenClaw, a popular open-source AI agent platform. It systematically introduces this intelligent agent framework—affectionately dubbed "Lobster Farming"—helping readers quickly grasp its core value, technical features, application scenarios, and security protocols. It serves as an excellent introductory guide and practical manual.
這是一張最近風靡全網關於熱門開源AI代理平台OpenClaw的全網應用全景圖解。它系統性地介紹了這款被稱為「養龍蝦」的智慧體框架,幫助讀者快速理解其核心價值、技術特性、應用場景及安全規範,是一份極佳的入門指南與實操手冊。此圖主要針對希望利用AI建構自動化工作流程的技術從業人員、中小企業主及效率追求者,透過9大模組層層遞進,全面剖析了OpenClaw從概念到落地的整個過程。 圖中核心內容首先釐清了「養龍蝦」指涉的是OpenClawd開源智能體,並強調其本質是「AI基建」而非一般聊天機器人。隨後詳細比較其與傳統AI助理的區別,擁有記憶管理、權限控制、會話隔離和異常恢復四大基礎能力,支援跨平台存取和多模型相容(如GPT、Claude、Ollama)。同時,圖解提供了完整的部署方案(雲端/本地/Docker),並列舉了辦公室自動化、內容創作、資料收集等五大應用程式場景。此外,還展示了其火爆程度、政府與大廠佈局、安全部署建議及適合/不適合的人群分類。幫助你快速掌握OpenClaw技術架構與應用價值,指導個人或企業建構AI自動化系統,規避資料外洩與權限失控風險,是學習「執行式AI」轉型的權威參考圖譜。
本圖由萬興腦圖繪製,是針對IT研發崗位的結構化個人履歷模板,完整涵蓋求職核心資訊模組。基本資訊區包含姓名、電話、信箱、求職意願及GitHub連結;專業概要要求以2-3句提煉核心優勢;工作經驗以「公司A高級Java開發工程師」為例,以「透過(行動),達成(量化成果)」格式呈現微服務架構設計、系統效能優化、團隊技術規範制定等職責,公司B經歷則聚焦功能模組開發與Elasticsearch搜尋優化;技能專長分程式語言、後端框架、中介軟體、資料庫、容器雲等維度,清楚展示技術堆疊;專案成果以「電商平台秒殺系統」為例,說明技術棧、架構設計、個人貢獻(Redis Lua庫存原子扣減)及KPI;教育背景包含一流大學電腦專業學歷,以及AWS認證解決方案架構師、軟考中級軟體設計師證書。模板邏輯嚴謹,涵蓋IT研發求職全流程關鍵訊息,幫助求職者清晰、量化展示專業能力。
Machine learning algorithm classification
Machine learning is classified according to training methods
Supervised Learning
Artificial Neural Network class
Backpropagation
Boltzmann Machine
Convolutional Neural Network
Hopfield Network
Multilayer Perceptron
Radial Basis Function Network (RBFN)
Restricted Boltzmann Machine
Recurrent Neural Network (RNN)
Self-organizing Map (SOM)
Spiking Neural Network
Bayesin
Naive Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes
Averaged One-Dependence Estimators (AODE)
Bayesian Belief Network (BBN)
Bayesian Network (BN)
Decision Tree class
Classification and Regression Tree (CART)
Iterative Dichotomiser3 (Iterative Dichotomiser 3, ID3)
C4.5 Algorithm
C5.0 Algorithm (C5.0 Algorithm)
Chi-squared Automatic Interaction Detection (CHAID)
Decision Stump
ID3 Algorithm
Random Forest
SLIQ (Supervised Learning in Quest)
Linear Classifier class
Fisher’s Linear Discriminant
Linear Regression
Logistic Regression
Multinomial Logistic Regression
Naive Bayes Classifier
Perception
Support Vector Machine
Unsupervised Learning
Artificial Neural Network class
Generative Adversarial Networks (GAN)
Feedforward Neural Network
Logic Learning Machine
Self-organizing Map
Association Rule Learning class
Apriori Algorithm
Eclat Algorithm
FP-Growth algorithm
Hierarchical Clustering
Single-linkage Clustering
Conceptual Clustering
Cluster analysis
BIRCH algorithm
DBSCAN algorithm
Expectation-maximization (EM)
Fuzzy Clustering
K-means algorithm
K-means Clustering
K-medians clustering
Mean-shift algorithm (Mean-shift)
OPTICS algorithm
Anomaly detection class
K-nearest Neighbor (KNN) algorithm
Local Outlier Factor (LOF) algorithm
Semi-supervised Learning
Generative Models
Low-density Separation
Graph-based Methods
Co-training
Reinforcement Learning
Q-learning
State-Action-Reward-State-Action (State-Action-Reward-State-Action, SARSA)
DQN (Deep Q Network)
Policy Gradients
Model Based RL
Temporal Differential Learning
Deep Learning
Deep Belief Machines
Deep Convolutional Neural Networks
Deep Recurrent Neural Network
Hierarchical Temporal Memory (HTM)
Deep Boltzmann Machine (DBM)
Stacked Autoencoder
Generative Adversarial Networks
Transfer Learning
Inductive Transfer Learning
Transductive Transfer Learning
Unsupervised Transfer Learning
Transitive Transfer Learning
Machine learning is classified according to problem solving
Two-class Classification
Two-class SVM: suitable for scenarios with many data features and linear models
Two-class Average Perceptron: Suitable for scenarios with short training time and linear models.
Two-class Logistic Regression: Suitable for scenarios with short training time and linear models
Two-class Bayes Point Machine: suitable for scenarios with short training time and linear models
Two-class Decision Forest: suitable for scenarios with short training time and accuracy
Two-class Boosted Decision Tree: suitable for scenarios with short training time, high accuracy, and large memory usage
Two-class Decision Jungle: suitable for scenarios with short training time, high accuracy, and small memory footprint
Two-class Locally Deep SVM: suitable for scenarios with many data features
Two-class Neural Network: suitable for scenarios with high accuracy and long training time
Multi-class Classification
Multiclass Logistic Regression: Suitable for scenarios with short training time and linear models
Multiclass Neural Network: suitable for scenarios with high accuracy and long training time
Multiclass Decision Forest: suitable for scenarios with high accuracy and short training time
Multiclass Decision Jungle: suitable for scenarios with high accuracy and small memory footprint
"One-vs-all Multiclass": depends on the effect of the two classifiers
Regression algorithm
Ordinal Regression: Suitable for scenarios where data is classified and sorted
Poisson Regression: Suitable for predicting the number of events
Fast Forest Quantile Regression: Suitable for scenarios of predicting distributions
Linear Regression: Suitable for scenarios with short training time and linear models
Bayesian Linear Regression: suitable for linear models and scenarios where the amount of training data is small
Neural Network Regression: suitable for scenarios with high accuracy and long training time
Decision Forest Regression: suitable for scenarios with high accuracy and short training time
Boosted Decision Tree Regression: suitable for scenarios with high accuracy, short training time, and large memory usage
Clustering algorithm
Hierarchical Clustering: suitable for scenarios with short training time and large amount of data
K-means algorithm: suitable for scenarios with high accuracy and short training time
Fuzzy clustering FCM algorithm (Fuzzy C-means, FCM): suitable for scenarios with high accuracy and short training time
SOM neural network (Self-organizing Feature Map, SOM): suitable for scenarios with long running time
Anomaly Detection
One-class support vector machine (One-class SVM): suitable for scenarios with many data features
PCA-based Anomaly Detection: suitable for scenarios with short training time