A learning machine that hates learning: a quick learning method that gambles

This paper presents a quick machine learning system inspired by human learning behavior. Data-mining systems based on machine-learning usually need a large number of iterations to acquire correct solutions, whereas people usually find appropriate hidden rules after only a small number of observations of the instances in a dataset. We think that this quick learning is the result of using tentative hypothesis as the data-model in the early steps of the learning. If the hypothesis happens to be accurate, the learning is completed immediately after the hypothesis is applied. We therefore reduce the effort required for learning by gambling on the possibility that the tentative hypothesis is accurate. Our new machine learning system emulates this process by minimizing an objective function that represents not only the likelihood of error but also the predicted learning-cost. In experiments, the new system yields appropriate solutions to function approximation problems with only a small number of observations of instances. This system is helpful for emergent problem solving.