Multilayer Perceptron Integrated Fuzzy Nearest Neighbor to Improve the Proficiency of CBR-Retrieval
نویسندگان
چکیده
منابع مشابه
Superlinear Parallelization of k-Nearest Neighbor Retrieval
With m processors available, the k-nearest neighbor classifier can be straightforwardly parallelized with a linear speed increase of factor m. In this paper we introduce two methods that in principle are able to achieve this aim. The first method splits the test set in m parts, while the other distributes the training set over m sub-classifiers, and merges their m nearest neighbor sets with eac...
متن کاملK-Nearest Neighbours Directed Noise Injection in Multilayer Perceptron Training
Training M. Skurichina1, .Raudys2 and R.P.W. Duin1 1Pattern Recognition Group, Department of Applied Physics, Delft University of Technology, P.O. Box 5046, 2600GA Delft, The Netherlands. E-mail: [email protected], [email protected] 2Department of Data Analysis, Institute of Mathematics and Informatics, Akademijos 4, Vilnius 2600, Lithuania. Email: [email protected] Abstract T...
متن کاملK-nearest Neighbors Directed Noise Injection in Multilayer Perceptron Training
The relation between classifier complexity and learning set size is very important in discriminant analysis. One of the ways to overcome the complexity control problem is to add noise to the training objects, increasing in this way the size of the training set. Both the amount and the directions of noise injection are important factors which determine the effectiveness for classifier training. ...
متن کاملFUZZY K-NEAREST NEIGHBOR METHOD TO CLASSIFY DATA IN A CLOSED AREA
Clustering of objects is an important area of research and application in variety of fields. In this paper we present a good technique for data clustering and application of this Technique for data clustering in a closed area. We compare this method with K-nearest neighbor and K-means.
متن کاملFuzzy-Rough Nearest-Neighbor Classification Approach
This paper proposes a new --rough nearest-neighbor (NN ) approach based on the fuzzy-rough sets theory. This approach is more suitable to be used under partially exposed and unbalanced data set compared with crisp NN and fuzzy NN approach. Then the new method is applied to China listed company financial distress prediction, a typical classification task under partially exposed and unbalanced le...
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: June 2020
سال: 2020
ISSN: 2582-2640
DOI: 10.36548/jscp.2020.2.003