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Artificial Neural Network in Modern World

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The bias shifts the decision boundary away from the origin and does not depend on any input value. The value of f(x) f ( x ) {displaystyle f(x)} (0 or 1) is used to classify x as either a positive or a negative instance, in the case of a binary classification problem. If b is negative, then the weighted combination of inputs must produce a positive value greater than |b| | b | {displaystyle |b|} in order to push the classifier neuron over the 0 threshold. Spatially, the bias alters the position (though not the orientation) of the decision boundary. The perceptron learning algorithm does not terminate if the learning set is not linearly separable. If the vectors are not linearly separable learning will never reach a point where all vectors are classified properly. In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function. The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network.

The choice of the ANN structure wich going to be used each time depends on the complexity of the classification problem. As we mentioned before if the input vector is not linearly separable , thy are not able to classified properly through the learning process. This is a result of the limitations that appears in Hebb and Delta rule, they don’t work with all training patterns. A solution to this problem is to move to a different structure ANN, which contains a hidden layer and use other learning method, like error backpropagation , an algorithm which minimise the total error.

In this classification task we going to apply supervised learning methods(SLM). The goal of SLM is to approximate the function so well that when you have new input data (x) that you can predict the output variables (Y) for that data. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.In the other hand, unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. With unsupervised learning it is possible to learn larger and more complex models than with supervised learning. This is because in supervised learning one is trying to find the connection between two sets of observations. The difficulty of the learning task increases exponentially in the number of steps between the two sets and that is why supervised learning cannot, in practice, learn models with deep hierarchies.

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