;best agreement" rule can only tolerate small amounts of noise, yet in some cases large amounts of noise can be tolerated. We show that the truth lies somewhere between these two alternatives. For uniform attribute noise, in which each attribute is flipped independently at random with the same probability

We study the problem of PAC-learning Boolean functions with random attribute noise under the uniform distribution. First, we define a noisy distance measure for function classes and show that if this measure is small for a class C and an attribute noise distribution D then C is not learnable

We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders ’ beliefs creates a risk in the price of the asset that deters rational

dealing with noise that is introduced in attributes. In this paper, we present a systematic evaluation on the effect of noise in machine learning. Instead of taking any unified theory of noise to evaluate the noise impacts, we differentiate noise into two categories: class noise and attribute noise

that has been contaminated with additive noise, the goal is to identify which elementary signals participated and to approximate their coefficients. Although many algorithms have been proposed, there is little theory which guarantees that these algorithms can accurately and efficiently solve the problem

. In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes

databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a

This paper studies the robustness of PAC learning algorithms when the instance space is f0; 1g n , and the examples are corrupted by purely random noise affecting only the attributes (and not the labels). For uniform attribute noise, in which each attribute is flipped independently at random

, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the cn2, id3 and aq algorithms are compared on three medical classification tasks.

We propose a theory of securities market under- and overreactions based on two well-known psychological biases: investor overconfidence about the precision of private information; and biased self-attribution, which causes asymmetric shifts in investors ’ confidence as a function of their investment