An empirical study of methods of handling missing values in a backpropagation neural network is presented. Neural networks can be applied to many real world systems to perform classification, pattern recognition or prediction on the basis of input data. However, many such applications cannot guarantee that the data provided to the network will be complete. The backpropagation network does not l...

Search engines perform the task of retrieving information related to the user-supplied query words. This task has two parts; one is finding ’featured words’ which describe an article best and the other is finding a match among these words to user-defined search terms. There are two main independent approaches to achieve this task. The first one, using the concepts of semantics, has been impleme...

Inspired by the dissipative quantum model of brain, we model the states of neural nets in terms of collective modes by the help of the formalism of Quantum Field Theory. We exhibit an explicit neural net model which allows to memorize a sequence of several informations without reciprocal destructive interference, namely we solve the overprinting problem in such a way last registered information...

As the number and diversity of text databases on the Internet increases rapidly, users are faced with finding the text databases that are relevant to the user query. Identifying the relevant text databases out of many candidates for a given query is called the text database discovery problem. In this paper, we propose a neural net based approach to the text database discovery problem. First, we...

Compressing neural nets is an active research problem, given the large size of state-of-the-art nets for tasks such as object recognition, and the computational limits imposed by mobile devices. Firstly, we give a general formulation of model compression as constrained optimization. This makes the problem of model compression well defined and amenable to the use of modern numerical optimization...

During the last years, a remarkable breakthrough has been made in AI domain thanks to artificial deep neural networks that achieved a great success in many machine learning tasks in computer vision, natural language processing, speech recognition, malware detection and so on. However, they are highly vulnerable to easily crafted adversarial examples. Many investigations have pointed out this fa...

For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a si...

The paper describes the operation principles of the evolutive neuro fuzzy filtering (ENFF) properties, which based on back propagation fuzzy neural net, this filter adaptively choose and emit a decision according with the reference signal changes of an external reference process, in order to actualize the best correct new conditions updating a process. This neural net fuzzy filter mechanism sel...

Recurrent nets are in principle capable to store past inputs to produce the currently desired output. This recurrent net property is used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps between relevant inputs and desired outputs. In this case, however, gradient descent learning methods take to much time. The learning ...

I present a general taxonomy of neural net architectures for processing time-varying patterns. This taxonomy subsumes many existing architectures in the literature, and points to several promising architectures that have yet to be examined. Any architecture that processes timevarying patterns requires two conceptually distinct components: a short-term memory that holds on to relevant past event...