Data Research Papers

Natural Language Processing

Hints of the outbreak are detected through the modified circumstances favoring the outbreaks, like the warm weather contributing to epidermal outbreaks or the loss of sanitation leading to cholera outbreaks typically relying on the routine ...

Pattern Recognition with Neural Networks in C++

From the Publisher: The addition of artificial neural network computing to traditionalpattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical gui...

Bayesian Nonparametric Federated Learning of Neural Networks

In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data...

Artificial Neural Networks in Medical Diagnosis

Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; da...

Using neural networks for data mining

Neural Networks and Pattern Recognition

Artificial Neural Networks

Slimmable Neural Networks

We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individua...

Pattern Recognition and Neural Networks

Multilayer perceptron and neural networks

Abstract:- The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning ...

Neural networks and statistical techniques: A review of applications

Neural networks for pattern recognition

From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probabi...

Rule extraction: Using neural networks or for neural networks?

Fundamentals of neural networks: architectures, algorithms, and applications

1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. Re...

Pattern Recognition and Neural Networks

This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously...

Neural Network for Pattern Recognition

As famously quoted by Margaret Heffernan: - “A great deal of creativity is about pattern recognition, and what you need to discern patterns is tons of data. Your mind collects that data by taking note of random details and anomalies easily ...

Back Propagation Neural Networks

(1998). Back Propagation Neural Networks. Substance Use & Misuse: Vol. 33, No. 2, pp. 233-270....

Federated Machine Learning

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to t...

Proceedings of the 22nd international conference on Machine learning

This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning, which was held at the University of Bonn in ...

Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making

Purpose Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. ...