Learning from Data: Artificial Intelligence and Statistics VDoug Fisher, Hans-J. Lenz Springer Science & Business Media, 6 de dez. de 2012 - 450 páginas Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks. |
Conteúdo
3 | |
A Causal Calculus for Statistical Research | 23 |
Testbed for Uncertain Inference 47 | 46 |
Modeling and Monitoring Dynamic Systems by Chain Graphs | 69 |
On Test Selection Strategies for Belief Networks | 89 |
A HillClimbing Approach for Optimizing Classification Trees | 109 |
Learning Bayesian Networks is NPComplete 121 | 120 |
Learning Possibilistic Networks from Data | 143 |
Statistical Analysis fo Complex Systems in Biomedicine 251 | 250 |
Learning in Hybrid Noise Environments Using Statistical Queries | 259 |
On the Statistical Comparison of Inductive Learning Methods | 271 |
Dynamical Selection of Learning Algorithms | 281 |
Learning Bayesian Networks Using Feature Selection | 291 |
Data Representations in Learning | 301 |
Rule Induction as Exploratory Data Analysis 313 | 312 |
A Comparative | 323 |
Structure Learning of Bayesian Networks by Hybrid Genetic | 165 |
Detecting Complex Dependencies in Categorical Data | 185 |
Classification Using Bayes Averaging of Multiple Relational | 207 |
Hierarchical Clustering of Composite Objects with a Variable | 229 |
Searching for Dependencies in Bayesian Classifiers | 239 |
An Environment for Implementing Intelligent | 333 |
Framework for a Generic Knowledge Discovery Toolkit | 343 |
Control Representation in an EDA Assistant | 353 |
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Learning from Data: Artificial Intelligence and Statistics V Doug Fisher,Hans-J. Lenz Visualização parcial - 1996 |
Termos e frases comuns
1996 Springer-Verlag analysis applied approach Artificial Intelligence artificial neural network attributes Bayesian classifier Bayesian networks belief function causal clusters complex component conditional conditional independence contingency table cross-validation database dataset decision problems decision tree defined denote dependencies described domain Equation error rate estimate evaluation experiments feature selection Figure Fisher and H.-J genetic algorithms given graph heuristic hypothesis improvement independent indicator valuation induction inference influence diagrams labelled learner learning algorithm Learning from Data linear loglinear model Machine Learning Markov measure method metric Morgan Kaufmann multiple node object observed optimal overfitting pairs parameters partition pattern performance ploxoma posterior probability predictive accuracy predictors probabilistic probability distribution problem procedure pruning recursive regression representation represented rules sample Section sequential significant similar space specific split statistical strategy structure subset techniques Theorem training data training examples training set values variables vector words