Artificial Intelligence

Advances in Computers, Vol. 24

Read or Download Advances in Computers, Vol. 24 PDF

Best artificial intelligence books

Elements of Artificial Intelligence: Introduction Using LISP (Principles of computer science series)

The breadth of assurance is greater than sufficient to provide the reader an outline of AI. An advent to LISP is located early within the booklet. even if a supplementary LISP textual content will be a good option for classes within which vast LISP programming is needed, this bankruptcy is adequate for newbies who're mostly in following the LISP examples stumbled on later within the ebook.

Paraconsistency: Logic and Applications (Logic, Epistemology, and the Unity of Science)

A good judgment is termed 'paraconsistent' if it rejects the guideline known as 'ex contradictione quodlibet', in accordance with which any end follows from inconsistent premises. whereas logicians have proposed many technically constructed paraconsistent logical structures and modern philosophers like Graham Priest have complicated the view that a few contradictions could be precise, and encouraged a paraconsistent common sense to house them, until eventually fresh instances those structures were little understood through philosophers.

Computational Contact and Impact Mechanics: Fundamentals of Modeling Interfacial Phenomena in Nonlinear Finite Element Analysis

Many actual structures require the outline of mechanical interplay throughout interfaces in the event that they are to be effectively analyzed. Examples within the engineered global variety from the layout of prosthetics in biomedical engi­ neering (e. g. , hip replacements); to characterization of the reaction and sturdiness of head/disk interfaces in computing device magnetic garage units; to improvement of pneumatic tires with greater dealing with features and elevated sturdiness in car engineering; to description of the adhe­ sion and/or relative slip among concrete and reinforcing metal in structural engineering.

Neural Networks and Speech Processing (The Springer International Series in Engineering and Computer Science)

We want to take this chance to thank all of these individ­ uals who helped us gather this article, together with the folks of Lockheed Sanders and Nestor, Inc. , whose encouragement and help have been vastly favored. furthermore, we wish to thank the contributors of the Lab­ oratory for Engineering Man-Machine structures (LEMS) and the heart for Neural technological know-how at Brown collage for his or her widespread and valuable discussions on a couple of themes mentioned during this textual content.

Additional resources for Advances in Computers, Vol. 24

Sample text

In that context, if a pattern is picked randomly from the patterns to be classified, the class to which it belongs is the realization of a discrete random variable. Similarly, the values of the features of a randomly chosen pattern can be viewed as realizations of random variable, which are usually continuous. For instance, in the example of discrimination between capacitors and integrated circuits (Fig. 16), the random variable “class” may be equal to 0 for a capacitor and to 1 for an integrated circuit, while the reflectivity R at the area A may be viewed as continuous random variables.

We will consider below the “black-box” modeling of the hydraulic actuator of a robot arm: the set of variables {x} has a single element (the angle of the oil valve), and the quantity of interest {zp } is the oil pressure in the actuator. ); such a model allows predictions of the boiling points of molecules that were not synthesized before. Several similar cases will be described in this book. Black-box models, as defined above, are in sharp contrast with knowledgebased models, which are made of mathematical equations derived from first principles of physics, chemistry, economics, etc.

The classifier is not necessarily expected to give a full answer to such a question: it may make a contribution to the answer. Actually, it is often the case that the classifier is expected to be a decision aid only, the decision being made by the expert himself. In the first applications of neural networks to classification, the latter were expected to give a definite answer to the classification problem. Since significant advances have been made in the understanding of neural network operation, we know that they are able to provide a much richer information than just a binary decision as to the class of the pattern of interest: neural networks can provide an estimation of the probability of a pattern to belong to a class (also termed posterior probability of the class).

Download PDF sample

Rated 4.00 of 5 – based on 22 votes