User:Dlzhangxg/Books/Machine Learning

Machine Learning

Introduction and Main Principles

Accuracy paradox

Curse of dimensionality

Data analysis

Data dredging

Inductive bias

Machine learning

No free lunch theorem

Occam's razor

Overfitting

Regularization (machine learning)

Ugly duckling theorem

Uncertain data

Background and Preliminaries

Knowledge discovery in Databases

Business analytics

Business intelligence

Data mining

Knowledge discovery

Pattern recognition

Predictive analytics

Predictive modelling

Reactive business intelligence

Reactive business intelligence

Reasoning

Abductive reasoning

Case-based reasoning

Causality

First-order logic

Inductive logic programming

Inductive reasoning

Reasoning system

Textual case based reasoning

Search Methods

Anytime algorithm

Beam search

Best-first search

Breadth-first search

Brute-force search

Depth-first search

Grid search

Hill climbing

Nearest neighbor search

Stochastic gradient descent

Tabu search

Statistics

Algorithmic inference

Base rate

Bayesian inference

Bias (statistics)

Covariate

Cross-entropy method

Expectation propagation

Expectation–maximization algorithm

Exploratory data analysis

Generative model

Gibbs sampling

Kullback–Leibler divergence

Latent variable

Maximum a posteriori estimation

Maximum likelihood

Statistical inference

Main Learning Paradigms

Active learning (machine learning)

Explanation-based learning

Hyperparameter optimization

Multi-task learning

Offline learning

Online learning model

Online machine learning

Reinforcement learning

Supervised learning

Transduction

Unsupervised learning

Classification Tasks

Binary classification

Calibration (statistics)

Class membership probabilities

Classification in machine learning

Concept class

Concept drift

Concept learning

Decision boundary

Feature space

Feature vector

Features (pattern recognition)

Multiclass classification

Prior knowledge for pattern recognition

Online Learning

Margin Infused Relaxed Algorithm

Semi-supervised learning

Coupled pattern learner

One-class classification

Semi-supervised learning

Lazy learning and nearest neighbors

Cluster assumption

Eager learning

IDistance

Instance-based learning

K-nearest neighbor algorithm

Large margin nearest neighbor

Lazy learning

Decision Trees

Adjusted mutual information

Alternating decision tree

C4.5 algorithm

CHAID

Decision stump

Decision tree learning

Grafting (decision trees)

ID3 algorithm

Incremental decision tree

Information Fuzzy Networks

Information gain in decision trees

Information gain ratio

Logistic model tree

Mutual information

Pruning (decision trees)

Random forest

Linear Classifiers

Linear classifier

Margin (machine learning)

Margin classifier

Soft independent modelling of class analogies

Statistical classification

Category utility

Discriminant function analysis

Discriminative model

Fisher kernel

Linear discriminant analysis

Multiclass LDA

Multilinear subspace learning

Multiple discriminant analysis

Optimal discriminant analysis

Probability matching

Quadratic classifier

Statistical classification

Variable kernel density estimation

Evaluation of Classification Models

Confusion matrix

Cross-validation (statistics)

Data classification (business intelligence)

F1 score

Generalization error

Hinge loss

Lift (data mining)

Loss function

Matthews correlation coefficient

Precision and recall

Receiver operating characteristic

Sensitivity and specificity

Stability in learning

Synthetic data

Test set

Training set

Type I and type II errors

Feature Creation and Optimization

Data Pre-processing

Diffusion map

Dimension reduction

Discretization of continuous features

Elastic map

Feature engineering

Feature extraction

Feature selection

Gaussian process

Gramian matrix

Isomap

Kernel adaptive filter

Kernel eigenvoice

Kernel principal component analysis

Locality-sensitive hashing

Manifold alignment

Minimum redundancy feature selection

Multidimensional scaling

Multifactor dimensionality reduction

Multilinear principal-component analysis

Nonlinear dimensionality reduction

Principal component analysis

Spectral clustering

Targeted projection pursuit

Clustering

BIRCH (data clustering)

Canopy clustering algorithm

Cluster analysis

Cluster-weighted modeling

Clustering high-dimensional data

Cobweb (clustering)

Complete-linkage clustering

Constrained clustering

Correlation clustering

CURE data clustering algorithm

Data stream clustering

DBSCAN

Dendrogram

Determining the number of clusters in a data set

FLAME clustering

Fuzzy clustering

Hierarchical clustering

Information bottleneck method

K-means clustering

K-means++

K-medians clustering

K-medoids

Lloyd's algorithm

Nearest-neighbor chain algorithm

Neighbor joining

OPTICS algorithm

Pitman–Yor process

Single-linkage clustering

SUBCLU

Thresholding (image processing)

UPGMA

Evaluation of Clustering Methods

Davies–Bouldin index

Dunn index

Jaccard index

K q-flats

MinHash

Rand index

Rule Induction

Classification rule

CN2 algorithm

Decision list

Decision rules

First Order Inductive Learner

Rule induction

Association rules and Frequent Item Sets

Affinity analysis

Apriori algorithm

Association rule learning

Contrast set learning

K-optimal pattern discovery

Ensemble Learning

AdaBoost

Boosting

Bootstrap aggregating

BrownBoost

Cascading classifiers

Co-training

CoBoosting

Consensus clustering

Ensemble averaging

Ensemble learning

Gaussian process emulator

Gradient boosting

LogitBoost

LPBoost

Mixture model

Product of Experts

Random multinomial logit

Random subspace method

Randomized weighted majority algorithm

Weighted Majority Algorithm

Graphical Models

Graphical model

State transition network

Bayesian Learning Methods

Averaged one-dependence estimators

Bayesian network

Naive Bayes classifier

Variational message passing

Markov Models

Baum–Welch algorithm

Conditional random field

Forward–backward algorithm

Hidden Markov model

Hierarchical hidden Markov model

Markov chain Monte Carlo

Markov logic network

Markov model

Markov random field

Maximum-entropy Markov model

Predictive state representation

Learning Theory

Bondy's theorem

Computational learning theory

Inferential theory of learning

Minimum description length

Probably approximately correct learning

Rademacher complexity

Sample exclusion dimension

Shattering (machine learning)

Subclass reachability

Teaching dimension

Uniform convergence (combinatorics)

Unique negative dimension

Vapnik–Chervonenkis theory

VC dimension

Version space

Witness set

Support Vector Machines

Empirical risk minimization

Kernel methods

Kernel trick

Least squares support vector machine

Relevance vector machine

Sequential minimal optimization

Structural risk minimization

Structured SVM

Support vector machine

Regression analysis

Additive model

Antecedent variable

Autocorrelation

Backfitting algorithm

Bayesian linear regression

Bayesian multivariate linear regression

Binomial regression

Canonical analysis

Censored regression model

Coefficient of determination

Comparison of general and generalized linear models

Compressed sensing

Conditional change model

Controlling for a variable

Cross-sectional regression

Curve fitting

Deming regression

Dependent and independent variables

Design matrix

Difference in differences

Dummy variable (statistics)

Errors and residuals in statistics

Errors-in-variables models

Explained sum of squares

Explained variation

First-hitting-time model

Fixed effects model

Fraction of variance unexplained

Frisch–Waugh–Lovell theorem

General linear model

Generalized additive model

Generalized additive model for location, scale and shape

Generalized estimating equation

Generalized least squares

Generalized linear array model

Generalized linear mixed model

Generalized linear model

Growth curve

Guess value

Hat matrix

Heckman correction

Heteroscedasticity-consistent standard errors

Hosmer–Lemeshow test

Instrumental variable

Interaction (statistics)

Isotonic regression

Iteratively reweighted least squares

Kitchen sink regression

Lack-of-fit sum of squares

Least squares

Leverage (statistics)

Limited dependent variable

Linear least squares (mathematics)

Linear model

Linear probability model

Linear regression

Local regression

Mallows's C_p

Mean and predicted response

Mixed model

Moderation (statistics)

Moving least squares

Multicollinearity

Multiple correlation

Multivariate adaptive regression splines

Multivariate probit

Newey–West estimator

Non-linear least squares

Nonlinear regression

Outline of regression analysis

Regression analysis

Logistic Regression

Logistic regression

Logit

Multinomial logit

Bio-inspired Methods

Bio-inspired computing

Evolutionary Algorithms

Chromosome (genetic algorithm)

Crossover (genetic algorithm)

Evolutionary algorithm

Evolutionary computation

Evolutionary data mining

Evolvability (computer science)

Fitness function

Genetic algorithm

Genetic programming

Learnable Evolution Model

Neural Networks

Activation function

ADALINE

Adaptive Neuro Fuzzy Inference System

Adaptive resonance theory

ALOPEX

Artificial Intelligence System

Artificial neural network

Artificial neuron

Attractor network

Autoassociative memory

Autoencoder

Backpropagation

Bcpnn

Bidirectional associative memory

Biological neural network

Boltzmann machine

Cellular neural network

Cerebellar Model Articulation Controller

Committee machine

Competitive learning

Compositional pattern-producing network

Computational cybernetics

Computational neurogenetic modeling

Confabulation (neural networks)

Cortical column

Counterpropagation network

Cover's theorem

Cultured neuronal network

Dehaene-Changeux Model

Delta rule

Early stopping

Echo state network

Evolutionary Acquisition of Neural Topologies

Extension neural network

Feed-forward

Feedforward neural network

Generalized Hebbian Algorithm

Generative topographic map

Group method of data handling

Growing self-organizing map

Helmholtz machine

Hierarchical temporal memory

Hopfield network

Hybrid neural network

HyperNEAT

Infomax

Instantaneously trained neural networks

Interactive Activation and Competition

IPO underpricing algorithm

Leabra

Learning Vector Quantization

Lernmatrix

Linde–Buzo–Gray algorithm

Liquid state machine

Long short term memory

Madaline

Memory-prediction framework

Modular neural networks

MoneyBee

Multilayer perceptron

Neocognitron

Nervous system network models

NETtalk (artificial neural network)

Neural backpropagation

Neural coding

Neural cryptography

Neural decoding

Neural gas

Neural Information Processing Systems

Neural modeling fields

Neural network

Neural oscillation

Neurally controlled animat

Neuroevolution of augmenting topologies

Neuroplasticity

Ni1000

Nonspiking neurons

Nonsynaptic plasticity

Oja's rule

Optical neural network

Perceptron

Phase-of-firing code

Promoter based genetic algorithm

Pulse-coupled networks

Quantum neural network

Radial basis function

Radial basis function network

Random neural network

Recurrent neural network

Reentry (neural circuitry)

Reservoir computing

Restricted Boltzmann machine

Rprop

Self-organizing map

Semantic neural network

Sigmoid function

SNARC

Softmax activation function

Spiking neural network

Stochastic neural network

Synaptic plasticity

Synaptic weight

Tensor product network

The Emotion Machine

Time delay neural network

Types of artificial neural networks

U-Matrix

Universal approximation theorem

Winner-take-all

Winnow (algorithm)

Reinforcement learning

Apprenticeship learning

Bellman equation

Markov decision process

Multi-armed bandit

Predictive learning

Q-learning

Reinforcement learning

SARSA

Temporal difference learning

Text Mining

Automatic summarization

Bag of words model

Biomedical text mining

Concept mining

Document classification

Information extraction

N-gram

Natural language processing

Never-Ending Language Learning

Part-of-speech tagging

Semantic analysis (machine learning)

Sentiment analysis

String kernel

Text mining

Topic model

Structure Mining

Process mining

Sequence labeling

Sequence mining

Structure mining

Structured learning

Structured prediction

Advanced Learning Tasks

Algorithmic learning theory

Alpha algorithm

Anomaly detection

Anomaly Detection at Multiple Scales

Biclustering

Bongard problem

Classifier chains

Co-occurrence networks

Conceptual clustering

Data stream mining

Deep learning

Formal concept analysis

Granular computing

GSP Algorithm

Inductive transfer

Information visualization

Learning automata

Learning to rank

Learning with errors

Local outlier factor

Meta learning (computer science)

Multi-label classification

Multiple-instance learning

Multispectral pattern recognition

Novelty detection

Optimal matching

Parity learning

Record linkage

Relational classification

Statistical relational learning

Syntactic pattern recognition

Web mining

Applications

Activity recognition

Anomaly detection

Behavioral targeting

Collaborative filtering

Computer vision

Data Analysis Techniques for Fraud Detection

Facial recognition system

Molecule mining

Novelty detection

Outlier detection

Proactive Discovery of Insider Threats Using Graph Analysis and Learning

Problem domain

Profiling (information science)

Recommender system

Robot learning

Speech recognition

Stock forecast