Incremental Local Linear Fuzzy Classifier in Fisher Space
نویسندگان
چکیده
منابع مشابه
Incremental Local Linear Fuzzy Classifier in Fisher Space
1 Faculty of Electrical Engineering, K.N. Toosi University of Technology, P. O. Box 16315-1355, Tehran, Iran 2Unité de Génie Biophysique et Médical, Groupe de Recherche sur l’Analyse Multimodale de la Fonction Cérébrale (GRAMFC), Faculté de Médecine, 3 rue des Louvels, 80036 AMIENS cedex, France 3 School of Information Technology and Engineering, University of Ottawa, 800 King Edward Avenue, Ot...
متن کاملBEST APPROXIMATION SETS IN -n-NORMED SPACE CORRESPONDING TO INTUITIONISTIC FUZZY n-NORMED LINEAR SPACE
The aim of this paper is to present the new and interesting notionof ascending family of $alpha $−n-norms corresponding to an intuitionistic fuzzy nnormedlinear space. The notion of best aproximation sets in an $alpha $−n-normedspace corresponding to an intuitionistic fuzzy n-normed linear space is alsodefined and several related results are obtained.
متن کاملIncremental classifier based on a local credibility criterion
In this paper we propose the Local Credibility Concept (LCC), a novel technique for incremental classifiers. It measures the classification rate of the classifier’s local models and ensures that the models do not cross the borders between classes, but allows them to develop freely within the domain of their own class. Thus, we reduce the dependency on the order of training samples, an inherent ...
متن کاملIncremental learning of feature space and classifier for face recognition
We have proposed a new approach to pattern recognition in which not only a classifier but also a feature space of input variables is learned incrementally. In this paper, an extended version of Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory (RAN-LTM) are effectively combined to implement this idea. Since IPCA updates a feature space increme...
متن کاملIncremental Learning of Local Linear Mappings
A new incremental network model for supervised learning is proposed. The model builds up a structure of units each of which has an associated local linear mapping (LLM). Error information obtained during training is used to determine where to insert new units whose LLMs are interpolated from their neighbors. Simulation results for several classiication tasks indicate fast convergence as well as...
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: EURASIP Journal on Advances in Signal Processing
سال: 2009
ISSN: 1687-6180
DOI: 10.1155/2009/360834