A Learning Algorithm based on High School Teaching Wisdom

TL;DR

This paper introduces a novel machine learning algorithm inspired by primary school teaching methods, emphasizing iterative evaluation and targeted training to enhance generalization and performance.

Contribution

It proposes a new incremental learning algorithm based on primary school teaching principles, improving generalization and data exploration in machine learning.

Findings

Enhanced learning curves with targeted training.

Improved generalization on high-variance data.

Applications in data mining and rare object detection.

Abstract

A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces better learning curves by demanding the student to optimally dedicate their learning time on the failed examples. When used in machine learning, the algorithm is found to train a machine on a data with maximum variance in the feature space so that the generalization ability of the network improves. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery.