MACHINE LEARNING TECHNIQUES - LASA

9
Performance
What would be an optimal learning curve?
What would be an optimal learning curve?
When is “ good enough ” achieved?
1. Time
How long can it take before an
acceptable level of
performance is achieved?
Figure I-1: Schematic of different learning curves
How much can one learn?
While humans and animals are in general very adaptive, they remain limited in what they can
adapt. Our body can adjust to fluctuations of temperatures, as long as these do not overshoot
certain limits in heat or coldness. Moreover, our ability to learn new things decreases over time.
Children up to 7 years old can easily learn two or more languages. After that age, they face the
same difficulty in acquiring a new language as adults do.
There is a reason for this. Searching for a solution is costly, both in time and in the number of
mistakes one can make during the search. As we grow older, we restrict the search space, in
order to allocate time to learning more complex things. If we were still trying to make sense of
syllables, we could never reach the level of understanding full sentences.
When is a good time to stop learning?
In humans, part of what makes us stop learning is a biological clock. Part of it is also related to
our own measure of performance. This measure comes mostly from comparison to others
(parents, peers, colleagues). Determining that you have reached an optimum, as well as
determining the best measure of performance is a crucial problem in machine learning.
Learning implies making mistakes
Inherent to the learning process is the notion of performance. A strong expectation is that, while
you learn, your performance improves continuously. However, there are situations where the
performance can degrade while one learns. For instance, when the search space contains
several optima, as illustrated in Figure I-1, while you search for a solution you may climb your
way up and down the optimality curve. In this class, we will see different methods to search for
the global optimum, by avoiding getting caught in local optima.
The performance and computational analysis of machine learning algorithms is a branch of
statistics known as learning theory. Machine learning algorithms take a training set, form
hypotheses or models, and make predictions about the future. Because the training set is finite
and the future is uncertain, learning theory usually does not yield absolute guarantees of
performance of the algorithms. Instead, probabilistic bounds on the performances of machine
learning algorithms are quite common.
© A.G.Billard 2004 – Last Update March 2011