On Learning Functions from Noise-Free and Noisy Samples via Occam's Razor
نویسنده
چکیده
learning theory, noise, non-linear filters An Occam approximation is an algorithm that takes as input a set of samples of a function and a tolerance e, and produces as output a compact representation of a function that is within e of the given samples. We show that the existence of an Occam approximation is sufficient to guarantee the probably approximate learnability of classes of functions on the reals even in the presence of arbitrarily large but random additive noise. One consequence of our results is a general technique for the design and analysis of non-linear filters in digital signal processing.
منابع مشابه
Conditions for Occam's Razor Applicability and Noise Elimination
The Occam's razor principle suggests that among all the correct hypotheses, the simplest hypothesis is the one which best captures the structure of the problem domain and has the highest prediction accuracy when classifying new instances. This principle is implicitly used also for dealing with noise, in order to avoid overrtting a noisy training set by rule truncation or by pruning of decision ...
متن کاملThe effects of traffic noise on memory and auditory-verbal learning in Persian language children
Background: Acoustic noise is one of the universal pollutants of modern society. Although the high level of noise adverse effects on human hearing has been known for many years, non-auditory effects of noise such as effects on cognition, learning, memory and reading, especially on children, have been less considered. Factors which have negative impact on these features can also have a negat...
متن کاملOccam's Razor in sensorimotor learning.
A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor--an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. He...
متن کاملExtending Occam's Razor
Occam's Razor states that, all other things being equal, the simpler of two possible hypotheses is to be preferred. A quanti ed version of Occam's Razor has been proven for the PAC model of learning, giving sample-complexity bounds for learning using what Blumer et al. call an Occam algorithm [1]. We prove an analog of this result for Haussler's more general learning model, which encompasses le...
متن کاملModel Selection of RBF Networks via Genetic Algorithms
One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately defining values for their free parameters. This work discusses how Radial Basis Function (RBF) neural networks can have their free parameters defined by Genetic Algorithms (GAs). For such, it firstly presents an overall view of the problems involved and the different approaches used to ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- SIAM J. Comput.
دوره 29 شماره
صفحات -
تاریخ انتشار 1999