Simple perceptron text classifier used for my class presentation. The program implements a single layer perceptron, the simplest neural network.
Each perceptron learns to distinguish a class from others and is fed input from .arff files(Atribute-Relation File Format). Note that the images shown below were made by me (Zamfir Alexandru) in Apple Keynote.
- it is easy to implement, serves as an introduction to neural networks;
- it makes use of parallelization, since each perceptron is independent from each other;
- it has a simple learning rule;
- Can only perfectly learn linearly separable data;
- Mixed (but generally good) results when learning non-linearly separable data;
In order to measure the performance of our program, we will be using the Confusion Matrix.
- Precision: Measures the proportion of true positive predictions among all positive predictions made by the model.
- Accuracy: Measures the overall correctness of the model's predictions.
- Recall (Sensitivity): Measures the proportion of true positive predictions among all actual positive instances.
- Specificity: Measures the proportion of true negative predictions among all actual negative instances.