What is Probabilistic Neural Network (PNN)?
A Probabilistic Neural Network is a type of feedforward neural network that uses a statistical algorithm called the Parzen window estimator to classify data points. PNNs are particularly effective in pattern recognition and classification problems. They are based on the principles of Bayesian networks and kernel methods, making them a powerful tool for probabilistic inference.
Key Components of PNNs
- Input Layer: This layer receives the input features of the data.
- Pattern Layer: Each neuron in this layer represents a training sample and computes the similarity between the input vector and the training sample using a kernel function.
- Summation Layer: This layer aggregates the outputs of the pattern layer neurons for each class.
- Output Layer: The final layer provides the probability of the input vector belonging to each class, and the class with the highest probability is chosen as the output.
Probabilistic Neural Networks: A Statistical Approach to Robust and Interpretable Classification
Probabilistic Neural Networks (PNNs) are a class of artificial neural networks that leverage statistical principles to perform classification tasks. Introduced by Donald Specht in 1990, PNNs have gained popularity due to their robustness, simplicity, and ability to handle noisy data. This article delves into the intricacies of PNNs, providing a detailed explanation, practical examples, and insights into their applications.
Table of Content
- What is Probabilistic Neural Network (PNN)?
- Bayes’ Rule in Probabilistic Neural Network
- How Does PNNs Work?
- Implementation of Probabilistic Neural Network
- Advantages and Disadvantages of PNNs
- Use-Cases and Applications of PNN