Leung, Y. “Feedforward Neural Network Models for Spatial Data Classification and Rule Learning”, in M.M. Fischer and A. Getis (eds.), Recent Developments in Spatial Analysis, Spatial Statistics, Behavioural Modelling and Computational Intelligence, Berlin: Springer-Verlag, 1997b, pp. 337–359...
Introduced Non-Linearity to Logistic Regression to form a Neural Network Types of Non-Linearity Sigmoid Tanh ReLU Feedforward Neural Network Models Model A: 1 hidden layer (sigmoid activation) Model B: 1 hidden layer (tanh activation) Model C: 1 hidden layer (ReLU activation) Model D: 2 hidd...
4.2 Sequential neural network models Sequential data is prevalent in many real-world problems and machine learning tasks—most popularly known for its role in natural language processing (NLP) and signal processing. Specifically, in chemical engineering, sequential data can exist in the form of sensor...
Computing the gradient is slightly more complicated for a neural network than other traditional machine learning models, but can still be done efficiently and exactly by the back propagation (BP) algorithm, i.e. computing the chain rule ∂Tn∂T0=∑path∂Tn∂Tn−1∂Tn−1∂Tn−2...
This paper proposes a framework for training feedforward neural network models capable of handling class overlap and imbalance by minimizing an error function that compensates for such imperfections of the training set. A special case of the proposed error function can be used for training variance-...
We have studied neural networks as models for time series forecasting, and our research compares the Box-Jenkins method against the neural network method for long and short term memory series. Our work was inspired by previously published works that yielded inconsistent results about comparative perfor...
Network models are trained and tested on UCI ECG arrhythmia data set. This data set is a good environment to test classifiers as it is incomplete and ambiguous bio-signal data collected from total 452 patient cases. The classification performance is evaluated using six measures; sensitivity, ...
Neural networks are the heart of deep learning models. They’re loosely inspired by how a human brain processes inputs to arrive at a conclusion. Technically speaking, just as the human brain comprises billions of neurons connected via synapses, an artificial neural network (ANN) consists of ...
(1998) we have argued that variable architecture models with random size hidden layer significantly reduce posterior multimodality typical for posterior distributions in neural network models. In this chapter we review the model proposed in Müller and Rios Insua (1998) and extend it to a non-...
Dropout can be intuitively explained as forcing the model to learn with missing input and hidden units. Or in other words, with different versions of itself. It provides a computationally inexpensive but powerful method of regularizing a broad family of neural network models during the training proc...