Supervised Models C1.6 Supervised composite networks
نویسنده
چکیده
Composite neural networks consist of multilayer networks, in which each layer may use different models of neurons: the classical sigmoidal neuron, the kernel neuron (like radial basis function neurons), the logical neuron, and so on. This section is devoted to supervised composite neural networks and contains three main parts. The first is focused on radial basis function (RBF) networks, as introduced by Poggio and Girosi. The second presents a special class of neural Bayesian classifier based on the kernel density estimator. In the third part, we briefly explain neural tree architectures, and the architecture of the well-known restricted Coulomb energy (RCE) algorithm, stressing their limitations. C1.6.
منابع مشابه
Unsupervised Models C2.3 Unsupervised composite networks
This section concerns neural networks which are hybrid either in terms of structure or in terms of training algorithms. The counterpropagation network is one that incorporates structural characteristics of the Kohonen and Grossberg networks and it is trained by composite supervised–unsupervised methods. The adaptive critic concept concerns neural network implementations of reinforcement learnin...
متن کاملComposite Kernel Optimization in Semi-Supervised Metric
Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...
متن کاملSemi-supervised training of a Kernel PCA-Based Model for Word Sense Disambiguation
In this paper, we introduce a new semi-supervised learning model for word sense disambiguation based on Kernel Principal Component Analysis (KPCA), with experiments showing that it can further improve accuracy over supervised KPCA models that have achieved WSD accuracy superior to the best published individual models. Although empirical results with supervised KPCA models demonstrate significan...
متن کاملINTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...
متن کاملMonthly runoff forecasting by means of artificial neural networks (ANNs)
Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...
متن کامل