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Many studies have shown that deep learning outperforms traditional machine learning methods in many applications. To prevent overfitting, a huge number of training samples is usually required in training process of deep learning. However, collecting such a large dataset is time-consumed and costly. Recently, several methods have been proposed to effectively learn the models with a limited number of...
Convolution neural networks (CNNs) eliminate the need for feature extraction which is one of the most important and time-consuming part of traditional machine learning (ML) methods. However, the challenge of training a deep CNN model with a limited amount of training data still remains. Transfer learning and parameter fine-tuning have emerged as solutions to this problem. Following the recent trends,...
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn relationships between attributes (physical and immunological) and the resulting tumour...
This study presents a new technique for identifying nonlinear systems using multiple models. In this technique the identification structure used is ANFIS, consequent parts are performed by multiple models and the interpolation of local models is performed by the membership functions of the Takagi Sugeno fuzzy system. The identification technique uses a number of multiple model concepts to initiate...
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