Titlebook: Soft Computing for Knowledge Discovery; Introducing Cartesia James G. Shanahan Book 2000 Springer Science+Business Media New York 2000 Baye

ARCHE

James G. Shanahanences has been disseminated on a worldwide basis by Plenum Publishing Cor­ poration of New York, and in the same year the coverage was broadened to include Canadian universities. All back issues can also be ordered from Plenum. We have reported in Volume 31 (thesis year 1986) a total of 11 ,480 theses titles 978-1-4615-7393-7978-1-4615-7391-3

上坡

been disseminated on a worldwide basis by Plenum Publishing Cor­ poration of New York, and in the same year the coverage was broadened to include Canadian universities. All back issues can also be ordered from Plenum. We have reported in Volume 31 (thesis year 1986) a total of 11 ,480 theses titles

敌意

Acetaldehyde

Machine Learningce at some task with experience. The goal of this chapter is to introduce techniques designed to acquire knowledge in this manner and to provide a framework for understanding the relationship among such methods, and in particular the machine learning approaches proposed and presented later in this book.

CLAM

Pander

Learning Cartesian Granule Feature ModelsG algorithm achieves this by embracing the synergistic spirit of soft computing, using genetic programming to discover the language (structure) of the model fuzzy sets and evidential rules for knowledge representation, while relying on the well-developed probability theory for learning the parameters of the model.

跳动

Knowledge Representationhuman understanding (not always required). Much of the work in knowledge representation is motivated by engineering concerns, with a little interest in psychological and linguistic plausibility [Hayes 1999]. Philosophers and psychologist have long pondered and debated how humans and other animals represent knowledge.

合乎习俗

Nonporous

Cartesian Granule Featuresn the next chapter. This approach to knowledge representation and related induction algorithms, while not being a panacea for knowledge discovery, do address some of the shortcomings of other knowledge discovery techniques such as decomposition error, and performance issues such as transparency, accuracy and efficiency.

Metamorphosis