Hidden neural networks

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.     

Evolutionary artificial neural networks

Evolutionary artificial neural networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This paper distinguishes among three levels of evolution in EANNs, i.e. the evolution of connection weights, architectures and learning rules. It first reviews each kind of evolution in detail and then analyses major issues related to each kind of evolution. It is shown in the paper that although there is a lot of work on the evolution of connection weights and architectures, research on the evolution of learning rules is still in its early stages. Interactions among different levels of evolution are far from being understood. It is argued in the paper that the evolution of learning rules and its interactions with other levels of evolution play a vital role in EANNs.     

Artificial neural networks in cancer research

Current therapy does not cure the majority of patients with B cell non-Hodgkin's lymphoma (NHL) and further intensification does not benefit the patient. Therefore, new approaches are necessary. Immunotherapy has become again a major interest as a new treatment modality for B cell lymphoma since the discovery that the lymphoma specific Id can be presented to antigen-specific T cells. Vaccination of the tumour-bearing host is one of the major strategies to induce a T cell mediated anti-tumour immunity in vivo. For B cell lymphomas the lymphoma specific Id can be used as a tumour-specific antigen to stimulate T cells. Alternatively, the malignant B cells can be modified to become efficient antigen presenting cells (APCs) and present peptides from their own tumour-specific antigens to the autologous T cells. Currently explored and future vaccination strategies for B cell lymphoma will be discussed here.