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Learning Binary Perceptrons Perfectly Efficiently
1996
Journal of computer and system sciences (Print)
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The majority rule algorithm for learning binary weights for a perceptron is analysed under the uniform distribution on inputs. ...
Particular consequences are that the algorithm PAC-learns majority functions in linear time from small samples and that, while the general variant of binary integer programming embodied here is NPcomplete ...
n.
375 LEARNING BINARY PERCEPTRONS PERFECTLY EFFICIENTLY 2 Pitt and Valiant [12] proved the NP-completeness of learning Boolean threshold functions (i.e., perceptrons with 0Â1 weights and integer ...
doi:10.1006/jcss.1996.0028
fatcat:gtrw3o7aofah7a5mji3673vxri
Quantum Neuron with Separable-State Encoding
[article]
2022
arXiv
pre-print
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We develop a hybrid (quantum-classical) training procedure for simulating the learning process of the QP and test their efficiency. ...
The proposed QP uses an N-ary encoding of the binary input data characterizing the patterns. ...
The fidelity constitutes a measure of the ability of the perceptron to adjust its weight to the target weight value. When f = 1, the perceptron can perfectly recognize the given pattern. ...
arXiv:2202.08306v1
fatcat:rslhvvdhubh7lkfqym4vdm6hqu
Implementing perceptron models with qubits
[article]
2019
arXiv
pre-print
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We propose a method for learning a quantum probabilistic model of a perceptron. ...
We show that this allows us to better capture noisyness in data compared to a classical perceptron. By considering entangled qubits we can learn nonlinear separation boundaries, such as XOR. ...
We show that the problem can be learned perfectly with two qubits.
V. ...
arXiv:1905.06728v1
fatcat:gstubszcnvcxhphneybncncn7e
Implementing perceptron models with qubits
2019
Physical Review A
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We propose a method for learning a quantum probabilistic model of a perceptron. ...
We show that this allows us to better capture noisiness in data compared to a classical perceptron. By considering entangled qubits we can learn nonlinear separation boundaries, such as XOR. ...
If the problem is linearly separable, the classical perceptron converges to a solution where the two classes are perfectly separated. ...
doi:10.1103/physreva.100.020301
fatcat:foc2ytirmzbhrclqo53wopnspa
A Capacity Scaling Law for Artificial Neural Networks
[article]
2018
arXiv
pre-print
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We derive the calculation of two critical numbers predicting the behavior of perceptron networks. First, we derive the calculation of what we call the lossless memory (LM) dimension. ...
The LM dimension is a generalization of the Vapnik--Chervonenkis (VC) dimension that avoids structured data and therefore provides an upper bound for perfectly fitting almost any training data. ...
The output of the encoder are the weights of a perceptron. The decoder receives the (perfectly learned) weights over a lossless channel. ...
arXiv:1708.06019v3
fatcat:7cbzywnqmfazld6nrl37wj2qge
A Practical Approach to Sizing Neural Networks
[article]
2018
arXiv
pre-print
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This allows the comparison of the efficiency of different network architectures independently of a task. ...
Based on MacKay's information theoretic model of supervised machine learning, this article discusses how to practically estimate the maximum size of a neural network given a training data set. ...
The output of the encoder are the weights of a perceptron. The decoder receives the (perfectly learned) weights over a lossless channel. ...
arXiv:1810.02328v1
fatcat:f4zh2nkitrhoff6oun7woe6ua4
Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
2022
Applied Sciences
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The purpose of this study is to develop accurate and efficient machine learning models for auditing appropriate surgical antimicrobial prophylaxis. ...
The efficient models developed by machine learning can be used to assist the antimicrobial stewardship team in the audit of surgical antimicrobial prophylaxis. ...
Therefore, it is helpful to develop efficient models by machine learning to analyze the big medical data associated with antimicrobial prophylaxis. ...
doi:10.3390/app12052586
fatcat:l7yoi4pnfbc53oiqe4bj53oyrm
Polyhedrons and Perceptrons Are Functionally Equivalent
[article]
2013
arXiv
pre-print
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The various constructions and results are among several steps required for algorithms that replace incremental and statistical learning with more efficient, direct and exact geometric methods for calculation ...
of perceptron architecture and weights. ...
It is a fact, however, that direct and efficient calculation of architecture and weights of DNF perceptron networks that perfectly recognize given data -and maintains margins, preset at will up to largest ...
arXiv:1311.1090v1
fatcat:tmpbamvfl5gkhb4cqd32s3gywa
Page 5143 of Mathematical Reviews Vol. , Issue 97H
[page]
1997
Mathematical Reviews
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binary perceptrons perfectly efficiently. ...
Summary: “The majority rule algorithm for learning binary weights for a perceptron is analysed under the uniform distri- bution on inputs. ...
Optimal colored perceptrons
2001
Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
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2000 accepted arXiv:cond-mat/0004173v2 fatcat:26cb74um35d3zkzxvre45gncd4Other Versions
Ashkin-Teller type perceptron models are introduced. Their maximal capacity per number of couplings is calculated within a first-step replica-symmetry-breaking Gardner approach. ...
This would parallel the situation for Hebb learning [13] . For model II we have for κ = 0 that 2.26 ≤ α c ≤ 2.28, which is larger than the maximal capacity of the standard binary perceptron. ...
In the light of these results an interesting question is whether such a coloured perceptron can still be more efficient than the standard perceptron. ...
doi:10.1103/physreve.64.011915
pmid:11461296
fatcat:qv7wgm7w6rfzjkc63u5f2kzhsi
ENTROPY-GUIDED FEATURE GENERATION FOR LARGE MARGIN STRUCTURED LEARNING
2018
Monografias em Ciência da Computação
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Structured learning consists in learning a mapping from inputs to structured outputs by means of a sample of correct input-output pairs. Many important problems fit in this setting. ...
Feature generation is an important subtask of structured learning modeling. ...
Structured Perceptron The structured perceptron algorithm [3] is analogous to its binary counterpart. ...
doi:10.17771/pucrio.dimcc.24327
fatcat:dtlkcg3m7bcbfigsrzvotok7tq
Modeling commonality among related classes in relation extraction
2006
Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
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For each class in the hierarchy either manually predefined or automatically clustered, a linear discriminative function is determined in a topdown way using a perceptron algorithm with the lower-level ...
This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. ...
Multi-Class Classification Basically, the perceptron algorithm is only for binary classification. ...
doi:10.3115/1220175.1220191
dblp:conf/acl/ZhouSZ06
fatcat:osjjbcmdyzb5xf3dxomelek2v4
Compressive neural representation of sparse, high-dimensional probabilities
[article]
2012
arXiv
pre-print
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Interestingly, functions satisfying the requirements of compressive sensing can be implemented as simple perceptrons. ...
If we use perceptrons as a simple model of feedforward computation by neurons, these results show that the mean activity of a relatively small number of neurons can accurately represent a high-dimensional ...
Here I have proposed an alternative mechanism by which the brain could efficiently represent probabilities: random perceptrons. ...
arXiv:1206.1800v1
fatcat:rwj4ybondbethcgojxubopfz4q
Social interaction as a heuristic for combinatorial optimization problems
2010
Physical Review E
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2010 pre-print arXiv:1009.1114v1 fatcat:f25fgix7g5bqrke2vkhzjhr7gaOther Versions
input patterns of size F by a Boolean Binary Perceptron. ...
Perceptron, given a fixed probability of success. ...
THE BOOLEAN BINARY PERCEPTRON The Boolean Binary Perceptron is a single-layer neural network whose weights are constrained to take on binary values only. ...
doi:10.1103/physreve.82.056118
pmid:21230556
fatcat:q3qyjnhfybgpbhuncuotrs3pr4
An Automatic System for Heart Disease Prediction using Perceptron Model and Gradient Descent Algorithm
2019
International Journal of Engineering and Advanced Technology
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The learning algorithm of Perceptron model which calculates the result and performs binary classification is ∑_(i=0)^n wixi >= b. ...
Many deep learning neural network-based models had been proposed for the prediction of heart diseases but there is no model accessing all 13 features directly from the dataset & feeding them to the 'Perceptron ...
It is an algorithm for supervised learning of binary classifiers. This Perceptron algorithm allows the neurons for learning and processing elements in the training set one after the other. ...
doi:10.35940/ijeat.a1278.109119
fatcat:slsxfdkfqbedjjv2ndma2ry2za
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