scholarly journals More efficient PAC-learning of DNF with membership queries under the uniform distribution

2004 ◽  
Vol 68 (1) ◽  
pp. 205-234 ◽  
Author(s):  
Nader H. Bshouty ◽  
Jeffrey C. Jackson ◽  
Christino Tamon
Keyword(s):  
Uniform Distribution ◽  
Pac Learning ◽  
Membership Queries

On Learning Perceptrons with Binary Weights

1993 ◽  
Vol 5 (5) ◽  
pp. 767-782 ◽  
Author(s):  
Mostefa Golea ◽  
Mario Marchand
Keyword(s):  
Learning Curve ◽  
Uniform Distribution ◽  
Pac Learning ◽  
Sample Complexity ◽  
Average Case Analysis ◽  
Average Case ◽  
Hebb Rule ◽  
Product Distributions ◽  
Training Examples ◽  
Learning Analysis

We present an algorithm that PAC learns any perceptron with binary weights and arbitrary threshold under the family of product distributions. The sample complexity of this algorithm is of O[(n/ε)4 ln(n/δ)] and its running time increases only linearly with the number of training examples. The algorithm does not try to find an hypothesis that agrees with all of the training examples; rather, it constructs a binary perceptron based on various probabilistic estimates obtained from the training examples. We show that, under the restricted case of the uniform distribution and zero threshold, the algorithm reduces to the well known clipped Hebb rule. We calculate exactly the average generalization rate (i.e., the learning curve) of the algorithm, under the uniform distribution, in the limit of an infinite number of dimensions. We find that the error rate decreases exponentially as a function of the number of training examples. Hence, the average case analysis gives a sample complexity of O[n ln(1/ε)], a large improvement over the PAC learning analysis. The analytical expression of the learning curve is in excellent agreement with the extensive numerical simulations. In addition, the algorithm is very robust with respect to classification noise.

PAC Learning Intersections of Halfspaces with Membership Queries

Algorithmica ◽  
1998 ◽  
Vol 22 (1-2) ◽  
pp. 53-75 ◽  
Author(s):  
S. Kwek ◽  
L. Pitt
Keyword(s):  
Pac Learning ◽  
Membership Queries

Levels taxation countries the EU and Ukraine’s

2019 ◽  
pp. 131-160
Author(s):  
Helena Borzenko ◽  
Tamara Panfilova ◽  
Mikhail Litvin
Keyword(s):  
European Union ◽  
Uniform Distribution ◽  
Tax Burden ◽  
Tax System ◽  
Tax Legislation ◽  
Improvement Process ◽  
Tax Systems ◽  
Historical Comparative ◽  
Different Levels ◽  
The Eu

Purpose articles rassm and experience and benefits systems taxation countries European Union, manifestation iti the main limitations domestic taxlegislation and wired STI their comparisons. In general iti ways the provisiontax reporting countries Eurozone in the appropriate organs, dove STI need theintroduction Ukraine electronic methods receiving and processing such reports.define iti key directions reforming domestic tax legislation. Methodology research is to use aggregate methods: dialectical, statistical, historical, comparative. Scientific novelty is to are provided recommendations for improvement ofefficiency systems taxation of our states in international ratings characterizingtax institutions country. Therefore, despite some problems in legislation heldcomparative study systems taxation EU and Ukraine. Conclucions Coming fromof this, the main directions reforming tax systems Ukraine, in our opinion,today should become: improvement process administration, reduce scales evasiontaxes, provision more uniform distribution tax burden between taxpayers, themaximum cooperation tax bodies different levels as well adjustment systemselectronic interactions tax authorities and payers, tax system must contain ascan less unfounded benefits, consistent with the general by politics pricing.

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