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• Generalizer: Definition of Generalizer in Webster's Dictionary 1913 Edition.
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• The Digital Librarian's Digital Library searchDOCIS Documents in Computing and Information Science Home >> Journals and Conference Proceedings >> Data Knowledge Engineering  A Query Generalizer for Providing Cooperative Responses in an Office Document System Qianhong Liu, Peter A. ...

• In this paper we address two crucial issues which have been considered to be a `black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. ...

•  Generalizer.

• The first technique discussed is cross-validation, which is a winner-takes-all strategy (based on the behavior of the generalizers on the partitions of the learning set, it picks one single generalizer from amongst the set of candidate generalizers, and tells you to use that generalizer). ... As opposed to cross-validation's winner-takes-all strategy, stacked generalization uses the partitions of the learning set to combine the generalizers, in a non-linear manner, via another generalizer (hence the term ``stacked generalization''). ...

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• KBG A Knowledge Based Generalizer 7 th International Conference on Machine Learning, B. ...
• KBG A Knowledge Based Generalizer 7 th International Conference on Machine Learning, B. ...

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• Generalizer.
• This is the definition of the term Generalizer.
• Generalizer (n. ...
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• Generalizer.
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• Naw, Clark is more of a Generalizer; I tend to think of myself as a "Field Marshaler. ...

• At least some of the pattern classification units include generalizer units having a memory for storing a number of generalizer "prototypes" and a comparator for comparing the vector location of an input pattern with each of the generalizer prototypes.
• (b) class selection means, responsive to all of said output signals Ri produced by said classification units Ui, for producing a single output response R representing the class of said pattern, the improvement wherein at least some of said pattern classification means include generalizer means comprising:.
• (i) generalizer memory means for storing a plurality of generalizer prototypes within a multi-dimensional pattern space, each generalizer prototype including, as prototype information stored in said generalizer memory means; a vector location in said pattern space; a scalar distance, in a given range between a minimum and maximum value, defining the size of a region of influence about said vector location of said generalizer prototype; and a particular class with which said generalizer prototype is associated; and.
• (ii) means for comparing the vector location of an input pattern, represented by said intermediate signal F, with each of said generalizer prototypes stored in said generalizer memory means to determine whether said input pattern location falls within a region of influence of at least one of said generalizer prototypes, and for producing an output classification response Ri, indicative of the class associated with such generalizer prototype, if said input pattern location falls within the region of influence thereof. ...
• The system defined in claim 1, wherein said pattern classification means which comprise generalizer means further include separator means comprising: .
• (i) separator memory means for storing a plurality of separator prototypes within said pattern space, each separator prototype including, as prototype information stored in said separator memory means; a vector location in said pattern space; a scalar distance defining the size of a region of influence about said vector location of said separator prototype; a particular class with which said separator prototype is associated; and the identity of a pair of generalizer prototypes with which said separator prototype is associated; and .
• The system defined in claim 1, where said generalizer means further include means for modifying said scalar distance defining the size of a region of influence of each generalizer prototype, within said given range, when said system is in a training mode. ...
• The system defined in claim 1, wherein said maximum value defines a region of influence for the respective generalizer prototype of approximately five percent to fifty percent of said pattern space. ...
• The system defined in claim 1, wherein each generalizer prototype further includes, as prototype information stored in said generalizer memory means, a set of weighting factors (W1, W2. ...
• The system defined in claim 8, further comprising means for adjusting said generalizer weighting factors, when in a training mode, to modify the output classification response Ri of said pattern classification means. ...
• The system defined in claim 2, wherein each pattern classification means which comprise generalizer means and separator means further include means for determining the pair of generalizer prototypes associated with each separator prototype. ...
• (a) storing in said memory means a plurality of generalizer prototypes within said multi-dimensional pattern space, each generalizer prototype including, as prototype information stored in said memory means; a vector location in said pattern space; a scalar distance, in a range between minimum and maximum value, defining the size of a region of influence about said vector location of said generalizer prototype; and a particular class with which said generalizer prototype is associated; .
• (b) comparing the location of an input pattern, represented by said input signal F, with each of said generalizer prototypes stored in said memory means to determine whether said input pattern location falls within a region of influence of at least one of said generalizer prototypes; and .
• (c) producing an output classification response Ri, indicative of the class associated with said at least one generalizer prototype, if said input pattern location falls within the region of influence thereof. ...
• (d) storing in said memory means a plurality of separator prototypes within said pattern space, each separator prototype including, as prototype information stored in said separator memory means: a vector location in said pattern space; a scalar distance defining the size of a region of influence about said vector location of said separator prototype; a particular class with which said separator prototype is associated and the identity of a pair of generalizer prototypes with which said separator prototype is associated; .
• The method defined in claim 11, further comprising the step of modifying said scalar distance defining the size of a region of influence of each generalizer prototype, within said given range, while in a training mode. ...

• A Domain Independent Explanation-Based Generalizer. ...
• This method is compared and contrasted with other approaches to generalizing explanations, including an abstract version of the algorithm used in the STRIPS system and the Expansion Based Generalizer technique recently developed. ...

• The other program, the interface, manages the user interface and gathers information about the user's preferences and activities; the actors involved include the user interface and the generalizer. ...
• The interface uses the pages in the database to prepare the window for the user and the generalizer adjusts the data in the database in response to user activity. ...
• This movement is very important information and is passed to the generalizer, which updates the database to reflect this change. ...
• The CloseUp needs a reference to the InterfaceWindow to notify the Generalizer of user activity by invoking methods in the InterfaceWindow. The Generalizer updates the database in response to user activity so that the system can better serve the user. The Generalizer registers with the InterfaceWindow, which implements the UserInterface. ...
• Cuurrently, the only listener is the Generalizer, who then uses the information to tailor the database in response to user activities. ...
• The CloseUp invokes the addURL() method of the InterfaceWindow to notify the Generalizer. The Generalizer then stores this information in the database to flag the Advisors to have the Ferrets gather these pages and force the Filters to accept them. ...

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