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Machine learning (Page 1)

scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions

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  3. Machine learning
Subconcepts:
  1. Active learning (machine learning)
  2. Activity recognition
  3. Adaptive algorithm
  4. Adaptive system
  5. Additive model
  6. Akaike information criterion
  7. Analysis of covariance
  8. Analysis of variance
  9. Anticipation (artificial intelligence)
  10. Applications of artificial intelligence
  11. Appropriate Use Criteria
  12. Artificial general intelligence
  13. Artificial immune system
  14. Artificial Intelligence System
  15. Artificial life
  16. Artificial neural network
  17. Association rule learning
  18. Bayesian information criterion
  19. Bayesian network
  20. Bayesian optimization
  21. Biological neural network
  22. Bionics
  23. Bivariate analysis
  24. Boosting (machine learning)
  25. Bootstrap aggregating
  26. Brier score
  27. Canonical analysis
  28. Case analysis
  29. Case-based reasoning
  30. Categorical variable
  31. Chemometrics
  32. Chow test
  33. Classification rule
  34. Classification scheme
  35. Cluster analysis
  36. Coefficient of determination
  37. Cohen's kappa
  38. Cointegration
  39. Collocation (remote sensing)
  40. Commonsense reasoning
  41. Component analysis
  42. Compositional data
  43. Compressed sensing
  44. Computational intelligence
  45. Computational neuroscience
  46. Computational Science and Engineering
  47. Computational statistics
  48. Concept learning
  49. Confusion matrix
  50. Contingency table
  51. Control variable
  52. Convolutional neural network
  53. Coordinate descent
  54. Correlation coefficient
  55. Correspondence analysis
  56. Covariate
  57. Credence
  58. Crossover
  59. Cross-validation
  60. Crowd sourcing
  61. Curse of dimensionality
  62. Curve fitting
  63. Data pre-processing
  64. Data stream mining
  65. Decision model
  66. Decision tree
  67. Deep learning
  68. Deviance (statistics)
  69. Dimensionality reduction
  70. Discrete choice
  71. Discriminant function analysis
  72. Discriminative model
  73. Distributed lag
  74. Dropout (neural networks)
  75. Dynamic mode decomposition
  76. Econometric model
  77. Educational data mining
  78. Empirical orthogonal functions
  79. Empirical risk minimization
  80. Endogeneity
  81. Ensemble forecasting
  82. Ensemble learning
  83. Epitome
  84. Errors-in-variables models
  85. Evolutionary algorithm
  86. Evolutionary computation
  87. Explained sum of squares
  88. Explained variation
  89. Exploratory search
  90. Exponential family
  91. F1 score
  92. Factor analysis
  93. Factorial experiment
  94. False alarm
  95. False positive paradox
  96. Feature learning
  97. Feature selection
  98. Feature vector
  99. Fisher information
  100. Free energy principle
  101. Functional data analysis
  102. Functional decomposition
  103. Galton's problem
  104. Gene expression programming
  105. Generalized additive model
  106. Generalized estimating equation
  107. Generalized linear mixed model
  108. Generalized linear model
  109. Genetic algorithm
  110. Genetic programming
  111. Geometric data analysis
  112. Geometric programming
  113. Gompertz function
  114. Go/no go
  115. Goodness of fit
  116. Granger causality
  117. Graphical model
  118. Ground truth
  119. Group method of data handling
  120. Hedonic regression
  121. Heteroscedasticity
  122. Hindcast
  123. Homogeneity (statistics)
  124. Hybrid system
  125. Hyperparameter
  126. Identifiability
  127. Incremental learning
  128. Independent component analysis
  129. Inductive logic programming
  130. Information filtering system
  131. Instrumental variable
  132. Intelligent agent
  133. Intelligent control
  134. Intelligent decision support system
  135. Interaction
  136. Interpretability
  137. k-nearest neighbors algorithm
  138. Kriging
  139. Labeled data
  140. Latent class model
  141. Latent growth modeling
  142. Latent variable
  143. Learnability
  144. Learning analytics
  145. Leverage (statistics)
  146. Lift (data mining)
  147. Linear model
  148. Linear regression
  149. Link analysis
  150. Logistic function
  151. Logistic regression
  152. Logit
  153. Main effect
  154. Margin (machine learning)
  155. Markov chain
  156. Mean squared prediction error
  157. Missing data
  158. Mixed model
  159. Model selection
  160. Moderated mediation
  161. Moderation
  162. Multi-agent system
  163. Multidimensional scaling
  164. Multi-label classification
  165. Multilevel model
  166. Multimodal learning
  167. Multinomial logistic regression
  168. Multi-objective optimization
  169. Multiple correspondence analysis
  170. Multivariate analysis
  171. Multivariate analysis of variance
  172. Multivariate probit model
  173. Multivariate statistics
  174. Music and artificial intelligence
  175. Natural computing
  176. Network structure
  177. News analytics
  178. NOMINATE
  179. Omitted-variable bias
  180. Optimal design
  181. Optimal estimation
  182. Optimality criterion
  183. Ordered logit
  184. Ordered probit
  185. Ordinal data
  186. Ordinal regression
  187. Ordinary least squares
  188. Ordination
  189. Partial least squares regression
  190. Particle swarm optimization
  191. Path analysis (statistics)
  192. Policy learning
  193. Precision and recall
  194. Prediction interval
  195. Predictive analytics
  196. Predictive modelling
  197. Predictive value of tests
  198. Probabilistic latent semantic analysis
  199. Probit
  200. Probit model
  201. Proxy (statistics)
  202. Qualitative comparative analysis
  203. Qualitative property
  204. Quantile regression
  205. Quantitative structure–activity relationship
  206. Random forest
  207. Rank correlation
  208. Ranking (information retrieval)
  209. Receiver operating characteristic
  210. Receptive field
  211. Recommender system
  212. Recursive partitioning
  213. Regression analysis
  214. Regret
  215. Reinforcement learning
  216. Response surface methodology
  217. Rough set
  218. Rule induction
  219. Scatter plot
  220. Schema (genetic algorithms)
  221. Score
  222. Scoring algorithm
  223. Scoring rule
  224. Seasonality
  225. Seemingly unrelated regressions
  226. Selection (genetic algorithm)
  227. Self-management
  228. Semantic mapping
  229. Semi-supervised learning
  230. Sensor array
  231. Sensor fusion
  232. Sentiment analysis
  233. Separation (statistics)
  234. Shrinkage
  235. Silhouette
  236. Simulated annealing
  237. Simulation-based optimization
  238. Small data
  239. Soar
  240. Social statistics
  241. Spearman's rank correlation coefficient
  242. Specification
  243. Spurious relationship
  244. Stability (learning theory)
  245. Standard score
  246. Statistical classification
  247. Statistical model
  248. Statistical noise
  249. Stepwise regression
  250. Structural equation modeling
  251. Structured prediction
  252. Studentized residual
  253. Subgradient method
  254. Support vector machine
  255. Surrogate model
  256. Taguchi methods
  257. Test set
  258. Threshold model
  259. Time series
  260. Tobit model
  261. Transfer of learning
  262. Trend analysis
  263. Truncation (statistics)
  264. Uncertain data
  265. Uncertainty quantification
  266. Universal Design for Learning
  267. Unsupervised learning
  268. Urban computing
  269. Value (mathematics)
  270. Variables
  271. Watershed
  272. Weighted arithmetic mean
  273. White box
Papers in this category: 3 171 073 Current Page: 1 / 100
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The Cityscapes Dataset for Semantic Urban Scene Understanding
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Machine learning in automated text categorization
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Data clustering: 50 years beyond K-means
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Papers in this category: 3 171 073 Current Page: 1 / 100