Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A fuzzy c-means classifier derived from a viewpoint of iteratively reighted least square techniques (IRLS-FCM) has been proposed, in which membership functions are variously chosen and parameterized.  focuses on the postsupervised classifier design and three kinds of regularization methods for classification are addressed: 1) the exponent of membership function or ights in entropy term in the FCM clustering, 2) the modification of covariance matrices in defining Mahalanobis distances and 3) the designing of intermediate classification rules beten linear and quadratic in the regularized discriminant analysis (RDA).  test the efficiency of these regularization methods in the IRLS-FCM classifier with regard to its performance and data set compression ratio, and show the best parameter values. Among the three regularization approaches, the improvement in classification performances is achieved mostly by the methods 1) and 2). Experiments on several ll-known benchmark data sets, shows that the FCM classifier using a newly defined membership function outperforms ll-established prototype-based methods, i.e., k-nearest neighbor classier (k-NN) and learning vector quantization (LVQ). Also concerning storage requirements and classification speed, the IRLS-FCM classifier gives a good performance and efficiency.
['Hidetomo Ichihashi', 'Katsuhiro Honda', 'Takao Hattori']
I am writing a research paper on a idea Regularized Discriminant in the Setting of Fuzzy c-Means Classifier
[{"from": "human", "value": "I am writing a research paper on a idea Regularized Discriminant in the Setting of Fuzzy c-Means Classifier"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A fuzzy c-means classifier derived from a viewpoint of iteratively reighted least square techniques (IRLS-FCM) has been proposed, in which membership functions are variously chosen and parameterized.  focuses on the postsupervised classifier design and three kinds of regularization methods for classification are addressed: 1) the exponent of membership function or ights in entropy term in the FCM clustering, 2) the modification of covariance matrices in defining Mahalanobis distances and 3) the designing of intermediate classification rules beten linear and quadratic in the regularized discriminant analysis (RDA).  test the efficiency of these regularization methods in the IRLS-FCM classifier with regard to its performance and data set compression ratio, and show the best parameter values. Among the three regularization approaches, the improvement in classification performances is achieved mostly by the methods 1) and 2). Experiments on several ll-known benchmark data sets, shows that the FCM classifier using a newly defined membership function outperforms ll-established prototype-based methods, i.e., k-nearest neighbor classier (k-NN) and learning vector quantization (LVQ). Also concerning storage requirements and classification speed, the IRLS-FCM classifier gives a good performance and efficiency."}]