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Neural network tree (NNTree) is a decision tree (DT) in which each internal node contains a neural network (NN). Experimental results show that the performance of the NNTrees is usually better than that of the traditional univariate DTs. In addition, the NNTrees are more usable than the single model fully connected NNs because their structures can be determined automatically in the induction process...
Support vector machine (SVM) is one of the best machine learning models that offers high accuracy both for recognition and for regression. One drawback of using SVM is that the system implementation cost is usually proportional to the number of training data and the dimension of the feature space. Therefore, it is difficult to use SVM in mobile devices such as IC cards and smart phones. In our study,...
In recent years, portable devices like IC cards and smart phones are becoming more and more popular. To use these kinds of devices more efficiently, it is desired to embed some intelligent agent (IA) in them. Support vector machine (SVM) is a kind of IA that can help a human user equipped with portable devices to be aware of different situations and make proper decisions. One problem in using SVM...
In neural network (NN) learning, we usually find an NN to minimize the approximation error for a given training set. Depends on the data given, the performance of the NN can vary significantly. In fact, if the training data are close to the true decision boundary (DB), the NN can generalize well. On the other hand, if the given data are far away from the true DB, the DB formed by the NN can be very...
Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. To increase the realizability of the NNTrees, we have tried to induce more compact NNTrees through dimensionality reduction. So far, we have used principal component analysis (PCA) and linear...
Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. Recently, we proposed an efficient algorithm for inducing the NNTrees based on a heuristic grouping strategy. In this paper, we try to induce smaller NNTrees based on dimensionality reduction...
Neural network tree (NNTree) is one of the efficient models for pattern recognition. One drawback in using an NNTree is that the system may become very complicated if the dimensionality of the feature space is high. To avoid this problem, we propose in this paper to reduce the dimensionality first using linear discriminant analysis (LDA), and then induce the NNTree. After dimensionality reduction,...
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