The statistical pattern recognition based on Bayes formula implies the concept of mutually exclusive classes. This assumption is not applicable when we have to identify some non-exclusive properties and therefore it is unnatural in biological neural networks. Considering the framework of probabilistic neural networks we propose statistical identification of non-exclusive properties by using one...

precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

In Neural Networks models the knowledge synthesized from the training process is represented in a subsymbolic fashion (weights, kernels, combination of numerical descriptions) that makes difficult its interpretation. The interpretation of the internal representation of a successful Neural Network can be useful to understand the nature of the problem and its solution, to use the Neural "model" a...

Perineuronal nets (PNNs) are presumed to limit plasticity in adult animals. Ischaemic stroke results the massive breakdown of PNNs resulting rejuvenating states neuronal plasticity, but mechanisms this phenomenon largely unknown. As hyaluronic acid (HA) is structural backbone PNNs, we hypothesized that these changes a consequence altered expression HA metabolism enzymes. Additionally, investiga...

Image segmentation is one of the most important methods for extracting information of interest from remote sensing image data, but it still remains some problems, leading to low quality segmentation. The research focuses on image segmentation based on PNNs and MLPNs. It presents to construct a PNN model and tunes a satisfied PNN for hyper-spectral image segmentation. Furthermore, the paper give...

In this paper, a system for automatic classification of musical instrument sounds is introduced. As features mel-frequency cepstral coefficients and as classifiers probabilistic neural networks are used. The experimental dataset included 4548 solo tones from 19 instruments of MIS database (The University of Iowa Musical Instrument Samples). Experiments for different system structures (hierarchi...

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task. Experiments...

We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a statistically justified subspace method of classification. The underlying structural mixture model includes binary structural parameters and can be optimized by EM algorithm in full generality. Formally, the structur...

The purpose of this paper is present probabilistic neural networks (PNN) as an alternative quantitative technique to both linear discriminant analysis (LDA) and backpropagated neural networks (BPNN) for forecasting corporate solvency. Although traditionally this task has been approached with rather simpler linear techniques such as LDA, there is increasing empirical evidence of the superiority ...