CH13_withFigures - Chapter 13 ADVANCED INTELLIGENT SYSTEMS...

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Chapter 13 ADVANCED INTELLIGENT SYSTEMS
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Learning Objectives Understand machine-learning concepts Learn the concepts and applications of case-based systems Understand the concepts and applications of genetic algorithms Understand fuzzy set theories and their applications in designing intelligent systems
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Learning Objectives Understand the concepts and applications of natural language processing (NLP) Learn the concepts, advantages, and limitations of voice technologies Learn about integrated intelligent support systems
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Machine-Learning Techniques Machine-learning concepts and definitions Machine learning The process by which a computer learns from experience (e.g., using programs that can learn from historical cases)
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Machine-Learning Techniques Human learning is a combination of many complicated cognitive processes including: Induction Deduction Analogy Other special procedures related to observing or analyzing examples
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Machine-Learning Techniques How learning relates to intelligent systems Learning systems demonstrate interesting learning behaviors AI is not able to learn as well as humans or in the same way that humans Machine learning cannot be applied in a creative way, although such systems can handle cases to which they have never been exposed It is not clear why learning systems succeed or fail A common thread running through most AI approaches to learning is the manipulation of symbols rather than numeric information
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Machine-Learning Techniques Machine-learning methods Supervised learning A method of training artificial neural networks in which sample cases are shown to the network as input and the weights are adjusted to minimize the error in its outputs Unsupervised learning A method of training artificial neural networks in which only input stimuli are shown to the network, which is self-
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Machine-Learning Techniques
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Machine-Learning Techniques Inductive learning Case-based reasoning Neural computing Genetic algorithms Natural language processing (NLP) Cluster analysis Statistical methods Explanation-based learning A machine learning approach that assumes that there is enough existing theory to rationalize why one instance is or is not a prototypical member of a class Machine-learning methods and algorithms
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Case-Based Reasoning (CBR) Case-based reasoning (CBR) A methodology in which knowledge and/or inferences are derived from historical cases
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Case-Based Reasoning (CBR) Analogical reasoning Determining the outcome of a problem with the use of analogies. A procedure for drawing conclusions about a problem by using past experience Inductive learning A machine learning approach in which rules are inferred from facts or data
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Case-Based Reasoning (CBR) The basic idea and process of CBR Four-step process 1. Retrieve 2. Reuse 3. Revise 4. Retain
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Case-Based Reasoning (CBR) Definition and concepts of cases in CBR
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CH13_withFigures - Chapter 13 ADVANCED INTELLIGENT SYSTEMS...

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