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On a Class of Stochastic Multilayer Networks

Bo Jiang, Philippe Nain, Don Towsley, Saikat Guha
2018 Performance Evaluation Review  
In this paper, we introduce a new class of stochastic multilayer networks.  ...  A stochastic multilayer network is the aggregation of M networks (one per layer) where each is a subgraph of a foundational network G.  ...  CONCLUSIONS In this work, we introduced a new class of stochastic multilayer networks.  ... 
doi:10.1145/3292040.3219667 fatcat:rjlq46b4gjg25lwrxhc3n4zjlm

On a Class of Stochastic Multilayer Networks

Bo Jiang, Philippe Nain, Don Towsley, Saikat Guha
2018 Proceedings of the ACM on Measurement and Analysis of Computing Systems  
In this paper, we introduce a new class of stochastic multilayer networks.  ...  A stochastic multilayer network is the aggregation of M networks (one per layer) where each is a subgraph of a foundational network G.  ...  CONCLUSIONS In this work, we introduced a new class of stochastic multilayer networks.  ... 
doi:10.1145/3179421 dblp:journals/pomacs/JiangNTG18 fatcat:vbtjrtvfrjfu7p2iqdg4yodcpu

On a Class of Stochastic Multilayer Networks [article]

Bo Jiang, Philippe Nain, Don Towsley, Saikat Guha
2018 arXiv   pre-print
In this paper, we introduce a new class of stochastic multilayer networks.  ...  A stochastic multilayer network is the aggregation of M networks (one per layer) where each is a subgraph of a foundational network G.  ...  CONCLUSIONS In this work, we introduced a new class of stochastic multilayer networks. Such a network is the aggregation of M random sub-networks of an underlying connectivity graph G.  ... 
arXiv:1807.03650v1 fatcat:6t2suu6tefhkfoqxqz4qwhgxxu

On a Class of Stochastic Multilayer Networks

Bo Jiang, Philippe Nain, Don Towsley, Saikat Guha
2018 Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems - SIGMETRICS '18  
In this paper, we introduce a new class of stochastic multilayer networks.  ...  A stochastic multilayer network is the aggregation of M networks (one per layer) where each is a subgraph of a foundational network G.  ...  CONCLUSIONS In this work, we introduced a new class of stochastic multilayer networks.  ... 
doi:10.1145/3219617.3219667 dblp:conf/sigmetrics/0003NTG18 fatcat:gtlhklf5mvgcthmjfslqhclyvu

A reinforcement learning approach based on the fuzzy min-max neural network

Aristidis Likas, Kostas Blekas
1996 Neural Processing Letters  
In this work, we elaborate further on the random hyperbox idea and propose the stochastic fuzzy min^max neural network, where each hyperbox is associated with a stochastic learning automaton.  ...  The fuzzy min^max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding  ...  The most widely used model of action selection network is the multilayer perceptron with stochastic output units.  ... 
doi:10.1007/bf00426025 fatcat:f7qqnlbncnfxvnug6mnvg66gou

Correspondence Analysis, Discrimination, and Neural Networks [chapter]

Ludovic Lebart
1998 Studies in Classification, Data Analysis, and Knowledge Organization  
Correspondence Analysis of contingency tables (CA) is closely related to a particular Supervised Multilayer Perceptron (MLP) or can be described as an Unsupervised MLP as well.  ...  The unsupervised MLP model is also linked to various types of stochastic approximation algorithms that mimic the cognition process involved in reading and comprehending a data table.  ...  CA can also be obtained from Linear Adaptive Networks (section 4), a series of methods closely related to stochastic approximation algorithms.  ... 
doi:10.1007/978-4-431-65950-1_47 fatcat:pft5zqemxnfzpch7ig4wxtqyla

Substantiation of the backpropagation technique via the Hamilton—Pontryagin formalism for training nonconvex nonsmooth neural networks

V.I. Norkin, V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, KyivNTU of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2019 Natsional'na Akademiya Nauk Ukrainy. Dopovidi: naukovyi zhurnal  
A method for calculating the stochastic generalized gradients of a learning quality functional for such systems is substantiated basing on the Hamilton-Pontryagin formalism.  ...  The paper observes the similarity between the stochastic optimal control over discrete dynamical systems and the lear ning multilayer neural networks.  ...  ., multilayer) neural networks, the stochastic gradient method and its modifications are mainly used [2, 3] , being adopted from the theory of stochastic approximation and stochastic programming, since  ... 
doi:10.15407/dopovidi2019.12.019 fatcat:bgaqz5hlinbbpnhbdyxcpt7z54

Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters

John S. Bridle
1989 Neural Information Processing Systems  
One of the attractions of neural network approaches to pattern recognition is the use of a discrimination-based training method.  ...  If the network is specially constructed to perform the recognition computations of a given kind of stochastic model based classifier then we obtain a method for discrimination-based training of the parameters  ...  INTRODUCTION It has often been suggested that one of the attractions of an adaptive neural network (NN) approach to pattern recognition is the availability of discrimination-based training (e.g. in Multilayer  ... 
dblp:conf/nips/Bridle89 fatcat:wtd4h3qre5hvlpavfymabfth4e

Breast Tumor Classification Using an Ensemble Machine Learning Method

Adel S. Assiri, Saima Nazir, Sergio A. Velastin
2020 Journal of Imaging  
Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble  ...  In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jimaging6060039 pmid:34460585 fatcat:4clkk5frbnemnfmv5pfc6okmhm

Multilayer Stochastic Block Models Reveal the Multilayer Structure of Complex Networks

Toni Vallès-Català, Francesco A. Massucci, Roger Guimerà, Marta Sales-Pardo
2016 Physical Review X  
First, we find the complete probabilistic solution to the problem of finding the optimal multilayer SBM for a given aggregate observed network.  ...  Here, we address the issue of uncovering the different interaction layers from aggregate data by introducing multilayer stochastic block models (SBMs), a generalization of single-layer SBMs that considers  ...  on a real multilayer network.  ... 
doi:10.1103/physrevx.6.011036 fatcat:ibtkfgp6rnfbffyryu3glc2cfq

A METHOD FOR CLASSIFYING ECG SIGNALS WITH DIFFERENT POSSIBLE STATES ON A MULTILAYER PERCEPTRON

Sherzod Nematov, Tashkent State Technical University, Y Talatov
2020 Technical science and innovation  
For this, the architecture of a neural network based on a multilayer perceptron and a method for obtaining the necessary parameters in the learning process have been developed, and the classification efficiency  ...  To automatically determine the state of the cardiovascular system based on the recorded ECG signals, an artificial neural network is trained to classify signals into various possible states.  ...  of one class during the training or testing process.  ... 
doi:10.51346/tstu-01.20.4-77-0077 fatcat:poe4wiefvrfwlclw6xhaugqc44

General Community Detection with Optimal Recovery Conditions for Multi-relational Sparse Networks with Dependent Layers [article]

Sharmodeep Bhattacharyya, Shirshendu Chatterjee
2020 arXiv   pre-print
We consider the problem of identifying the common community structure for a special type of multilayer networks called multi-relational networks.  ...  multi-relational networks generated from multilayer versions of both stochastic and degree-corrected block models even with dependence between network layers.  ...  We simulate such multilayer networks from multilayer stochastic block model (SBM) and multilayer degreecorrected block model (DCBM) under the framework of (2.2) and (2.5) of §2.  ... 
arXiv:2004.03480v1 fatcat:3y3rmklfg5chthxkicxpedtrv4

NWS volume 5 issue 2 Cover and Back matter

2017 Network Science  
org/core Special Issue on Modeling, Analysis, and Mining of Multilayer Networks edited by Matteo Magnani and Stanley Wasserman C O N T E N T S Articles Introduction to the special issue on multilayer networks  ...  matteo magnani and stanley wasserman A local perspective on community structure in multilayer networks Mathematics lucas g. s. jeub, michael w. mahoney, peter j. mucha and 141 mason a. porter Books and  ... 
doi:10.1017/nws.2017.18 fatcat:xruqesixr5h3vlbuxogugygfai

Editorial introduction

Masakiyo Miyazawa, Yiqiang Zhao
2013 Queueing systems  
This special issue focuses on a stochastic network described by a multidimensional stochastic process with reflecting boundaries.  ...  However, this subject has not yet been fully studied for a wide class of the stochastic networks because of technical difficulties.  ...  Haddad and Mazumdar consider a large stochastic fluid system, single link or a certain tree network, that operates under a balanced fair bandwidth allocation policy.  ... 
doi:10.1007/s11134-013-9356-8 fatcat:g54wnhwin5hq3mx267r22x6eny

Graphology Analysis and Identification of Personality Profile using Task fMRI

2020 International Journal of Emerging Trends in Engineering Research  
the process and reduce error .The dataset consists of 129 different person's handwriting on a phone note or tablet pc.  ...  The independent components obtained in analysis of task fMRI and the features obtained by the recognition are fused to classify the emotional state using Deep learning multilayer perceptron as positive  ...  Non linear networks is usually multiple local minima of different depths, The objective of training is to find one of these minima.Deeper local minima are produced by stochastic learning.  ... 
doi:10.30534/ijeter/2020/1298102020 fatcat:2q5az22crjf5vcegrhv2yzf63a
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