Janet S Cottage

First published in 1996. Routledge is an imprint of Taylor & Francis, an informa company.

Issued in conjunction with the exhibition at The Metropolitan Museum of Art of over 450 works of art from the legendary Havemeyer collection, formed at the turn of the century by pioneering American patrons of art Henry O. and Louisine Havemeyer, this lavishly illustrated catalogue combines 800 illustration (176 in color) with the collaborative efforts of 27 authors who examine the various aspects of the collection in summarizing essays and in entries on individual works. In addition, one essay is devoted to the Manhattan residence designed for the Havemeyers by Tiffany and Colman. An exhaustive 90-page chronology offers a perspective on the formation of the collection, outlining the roles of friend and advisor Mary Cassatt and a succession of dealers, and focusing on the history of the family and its business interests. 9.25x12.25" Annotation copyright by Book News, Inc., Portland, OR

Think you know everything there is to know about Hammer Films, the fabled "Studio that Dripped Blood?" The lowdown on all the imperishable classics of horror, like The Curse of Frankenstein, Horror of Dracula and The Devil Rides Out? What about the company's less blood-curdling back catalog? What about the musicals, comedies and travelogues, the fantasies and historical epics--not to mention the pirate adventures? This lavishly illustrated encyclopedia covers every Hammer film and television production in thorough detail, including budgets, shooting schedules, publicity and more, along with all the actors, supporting players, writers, directors, producers, composers and technicians. Packed with quotes, behind-the-scenes anecdotes, credit lists and production specifics, this all-inclusive reference work is the last word on this cherished cinematic institution.

Case-based reasoning means reasoning based on remembering previous experiences. A reasoner using old experiences (cases) might use those cases to suggest solutions to problems, to point out potential problems with a solution being computed, to interpret a new situation and make predictions about what might happen, or to create arguments justifying some conclusion. A case-based reasoner solves new problems by remembering old situations and adapting their solutions. It interprets new situations by remembering old similar situations and comparing and contrasting the new one to old ones to see where it fits best. Case-based reasoning combines reasoning with learning. It spans the whole reasoning cycle. A situation is experienced. Old situations are used to understand it. Old situations are used to solve a problem (if there is one to be solved). Then the new situation is inserted into memory alongside the cases it used for reasoning, to be used another time. The key to this reasoning method, then, is remembering. Remembering has two parts: integrating cases or experiences into memory when they happen and recalling them in appropriate situations later on. The case-based reasoning community calls this related set of issues the indexing problem. In broad terms, it means finding in memory the experience closest to a new situation. In narrower terms, it can be described as a two-part problem: assigning indexes or labels to experiences when they are put into memory that describe the situations to which they are applicable, so that they can be recalled later; and at recall time, elaborating the new situation in enough detail so that the indexes it would have if it were in the memory are identified. Case-Based Learning is an edited volume of original research comprising invited contributions by leading workers. This work has also been published as a special issues of MACHINE LEARNING, Volume 10, No. 3.