20 STATISTICAL LEARNING METHODS
Statistical Learning 715 ing, because it requires solving an optimization problem instead of a large summation (or integration) problem. We will see examples of this later in the chapter. In both Bayesian learning and MAP learning, the hypothesis prior P(hi) plays an im-portant role. We saw in Chapter 18 that overfitting can occur when the ...
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INTRODUCTION
aima.cs.berkeley.eduCHAPTER 3 SOLVING PROBLEMS BY SEARCHING function BEST-FIRST-SEARCH(problem,f) returns a solution node or failure node ←NODE(STATE=problem.INITIAL) frontier ←a priority queue orderedby f, with node as an element reached ←a lookup table, with one entry with key problem.INITIAL and value node while not IS-EMPTY(frontier) do node ←POP(frontier) if …
5 CONSTRAINT SATISFACTION PROBLEMS
aima.cs.berkeley.edumaximum domain size of any variable in a CSP is d, then the number of possible complete assignments is O(dn)—that is, exponential in the number of variables. Finite-domain CSPs BOOLEAN CSPS include Boolean CSPs, whose variables can be …
Artificial Intelligence
aima.cs.berkeley.eduPEARSONSERIES IN ARTIFICIAL INTELLIGENCE Stuart Russell and Peter Norvig, Editors FORSYTH & PONCE Computer Vision: A Modern Approach, 2nd ed. GRAHAM ANSI Common Lisp JURAFSKY & MARTIN Speech and Language Processing, 2nd ed. NEAPOLITAN Learning Bayesian Networks RUSSELL & NORVIG Artificial Intelligence: A Modern Approach, 4th ed.
7 LOGICAL AGENTS
aima.cs.berkeley.eduall the basic concepts of logic. There is also a well-developed technology for reasoning in propositional logic, which we describe in sections 7.5 and 7.6. Finally, Section 7.7 combines the concept of logical agents with the technology of propositional logic to build some simple agents for the wumpus world.
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