Border Pairs Method (BPM) is a new constructive method for supervised learning of multilayer perceptron (MLP), which calculates, values of weights and biases directly from the geometry of learning patterns.
To
determine BPM’s capabilities, we compared it with three other supervised machine
learning methods: Backpropagation , SVM and
Decision Trees. The comparison were made on six databases: XOR, Triangle, Iris, Pen-Based
Recognition of Handwritten Digits, Online Pen-Based Recognition of Handwritten
Digits and synthetically generated noisy data. Border
Pairs Method found near minimal MLP architecture in all described cases.
For classification of the Iris Setosa only two border pairs (only four patterns
out of 150) were enough for learning the whole data set correctly. In the
classification of ‘Pen-Based Recognition of Handwritten Digits’ dataset only
200 learning patterns were used for learning. The BPM correctly identified more
than 95% from 3498 handwritten digits, which did not participate in the learning proces.
We are particularly satisfied with the results of online learning. The adaptability
of the BPM to new incoming patterns is so high, that it easily follows and
sometimes even exceeds the offline version of the algorithm. Even with a noisy
data, some very good results were obtained. RMSE error of the BPM was
significantly lower than that of Backpropagation, especially for the noise lower
than 10%.
The initial
results of the research are good starting point for further research work. Some possible directions are:
· Noise reduction using border pairs.
· Algorithm improvement - better integration of border pairs.
Link to the scientific paper
Link to the Book
Bojan PLOJ, PhD
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