Supervised Classification Using Thresholded Neuron
Introduction In supervised classification using a threshold Neuron the inputs are assumed to have a fixed weights, so it does not learn, and the classification is made by the designer of the model. The threshold is chosen depending on the net input function (U) values. Objectives Understand the concepts of supervised classifications using single threshold and linear learning approach, and building a classifier via training. Methodology Using MATLAB code to compute linear model using given training data. then manually picking a threshold value that has been used to classify the data. Figure 1 MATLAB code to compute and plot. The data are plotted in scatter manner to show its distribution. Then the decision line is obtained from the line equation of a slope of 1 (equal weights). Observations/Measurements/Data sources Using the given training data set: Table 1 the training data Pattern No. Feature-x1 Feature-x2 Target (t) 1 0.5 0.1 1 2 0.6 0.2 1 3 0.4 0.3 1 4 1.5 1.9 0 5 1.6 1.8 0 To cl...
