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383,035
The Difficulty of Random Attribute Noise
, 1991
"... 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 instances (and not the labels). In the past, conflicting results on this subject have been obtained---the "best agreement ..."
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Cited by 1 (0 self)
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;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
Uniform-Distribution Attribute Noise Learnability
- Workshop on Computational Learning Theory
, 1999
"... 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 with r ..."
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Cited by 6 (0 self)
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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
Noise Trader Risk in Financial Markets
- Jolurnial of Political Economy
, 1990
"... 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 ..."
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Cited by 858 (23 self)
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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
Class Noise vs. Attribute Noise: A Quantitative Study of Their Impacts
- Artificial Intelligence Review
"... Abstract. Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification accuracy, time in building a classif ..."
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Cited by 57 (6 self)
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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
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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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
Estimating Attributes: Analysis and Extensions of RELIEF
, 1994
"... . 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. Origi ..."
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Cited by 450 (23 self)
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. 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
Instance-based learning algorithms
- Machine Learning
, 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
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Cited by 1359 (18 self)
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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
Can PAC Learning Algorithms Tolerate Random Attribute Noise?
- Algorithmica
, 1995
"... 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 with ..."
Abstract
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Cited by 36 (6 self)
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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
The CN2 Induction Algorithm
- MACHINE LEARNING
, 1989
"... Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensib ..."
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Cited by 884 (6 self)
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, 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.
Investor psychology and security market under- and overreactions
- Journal of Finance
, 1998
"... 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 ..."
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Cited by 661 (38 self)
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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
Results 1 - 10
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383,035