Prototype generation on structural data using dissimilarity space representation
نویسندگان
چکیده
منابع مشابه
Prototype Generation on Structural Data Using Dissimilarity Space Representation: A Case of Study
Data Reduction techniques are commonly applied in instancebased classification tasks to lower the amount of data to be processed. Prototype Selection (PS) and Prototype Generation (PG) constitute the most representative approaches. These two families differ in the way of obtaining the reduced set out of the initial one: while the former aims at selecting the most representative elements from th...
متن کاملPrototype-Based Classification of Dissimilarity Data
Unlike many black-box algorithms in machine learning, prototype based models offer an intuitive interface to given data sets since prototypes can directly be inspected by experts in the field. Most techniques rely on Euclidean vectors such that their suitability for complex scenarios is limited. Recently, several unsupervised approaches have successfully been extended to general possibly non-Eu...
متن کاملThe Dissimilarity Representation for Structural Pattern Recognition
The patterns in collections of real world objects are often not based on a limited set of isolated properties such as features. Instead, the totality of their appearance constitutes the basis of the human recognition of patterns. Structural pattern recognition aims to find explicit procedures that mimic the learning and classification made by human experts in well-defined and restricted areas o...
متن کاملOn using prototype reduction schemes to optimize dissimilarity-based classification
The aim of this paper is to present a strategy by which a new philosophy for pattern classification, namely that pertaining to dissimilaritybased classifiers (DBCs), can be efficiently implemented. This methodology, proposed by Duin and his co-authors (see Refs. [Experiments with a featureless approach to pattern recognition, Pattern Recognition Lett. 18 (1997) 1159–1166; Relational discriminan...
متن کاملOn Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes
The aim of this paper is to present a strategy by which a new philosophy for pattern classification, namely that pertaining to Dissimilarity-Based Classifiers (DBCs), can be efficiently implemented. This methodology, proposed by Duin and his co-authors (see [3], [4], [5], [6], [8]), is a way of defining classifiers between the classes, and is not based on the feature measurements of the individ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2016
ISSN: 0941-0643,1433-3058
DOI: 10.1007/s00521-016-2278-8