Open AccessBook
Pattern Recognition with Fuzzy Objective Function Algorithms
Reads0
Chats0
TLDR
Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.Abstract:
New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.read more
Citations
More filters
Journal ArticleDOI
Two cooperative ant colonies for feature selection using fuzzy models
TL;DR: This paper proposes an algorithm for feature selection based on two cooperative ant colonies, which minimizes two objectives: the number of features and the classification error.
Journal ArticleDOI
Joint inversion of multiple geophysical data using guided fuzzy c-means clustering
Jiajia Sun,Yaoguo Li +1 more
TL;DR: In this article, a joint inversion of multiple geophysical data has become an active area of research due to its potential to greatly enhance the fidelity of inverted models, and the authors have developed an approach that handles multimodal petrophysical information through guided fuzzy c-means clustering in the parameter domain.
Journal ArticleDOI
A hybrid approach to semantic web services matchmaking
TL;DR: This paper presents a hybrid framework which achieves a fuzzy matchmaking of semantic web services, where central role is entrusted to task-oriented agents that, given a service request, interact to discover approximate reply, when no exact match occurs among the available web services.
Journal Article
Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures
Zeynel Cebeci,Figen Yildiz +1 more
TL;DR: When well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.
Journal ArticleDOI
A Collaborative Fuzzy Clustering Algorithm in Distributed Network Environments
TL;DR: A novel collaborative fuzzy clustering algorithm is proposed, in which the centralized clustering solution is approximated by performing distributed clustering at each peer with the collaboration of other peers.
References
More filters
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.