scholarly journals Processing Heterogeneous RDF Events with Standing SPARQL Update Rules

2012 ◽  
pp. 797-806 ◽  
Author(s):  
Mikko Rinne ◽  
Haris Abdullah ◽  
Seppo Törmä ◽  
Esko Nuutila
Keyword(s):  
Update Rules

scholarly journals Resilient consensus of second-order agent networks: Asynchronous update rules with delays

Automatica ◽  
2017 ◽  
Vol 81 ◽  
pp. 123-132 ◽  
Author(s):  
Seyed Mehran Dibaji ◽  
Hideaki Ishii
Keyword(s):  
Second Order ◽  
Update Rules ◽  
Agent Networks

scholarly journals Sparsity-Constrained NMF Algorithm Based on Evolution Strategy for Hyperspectral Unmixing

2018 ◽  
Vol 232 ◽  
pp. 04019
Author(s):  
ShangBin Ning ◽  
FengChao Zuo
Keyword(s):  
Optimization Technique ◽  
Differential Evolution Algorithm ◽  
Real Data ◽  
Coefficient Matrix ◽  
Selection Strategy ◽  
Blind Separation ◽  
Basis Matrix ◽  
Hyperspectral Unmixing ◽  
Update Rules ◽  
Multiplicative Update

As a powerful and explainable blind separation tool, non-negative matrix factorization (NMF) is attracting increasing attention in Hyperspectral Unmixing(HU). By effectively utilizing the sparsity priori of data, sparsity-constrained NMF has become a representative method to improve the precision of unmixing. However, the optimization technique based on simple multiplicative update rules makes its unmixing results easy to fall into local minimum and lack of robustness. To solve these problems, this paper proposes a new hybrid algorithm for sparsity constrained NMF by intergrating evolutionary computing and multiplicative update rules (MURs). To find the superior solution in each iteration,the proposed algorithm effectively combines the MURs based on alternate optimization technique, the coefficient matrix selection strategy with sparsity measure, as well as the global optimization technique for basis matrix via the differential evolution algorithm .The effectiveness of the proposed method is demonstrated via the experimental results on real data and comparison with representative algorithms.

NONLINEAR GATED EXPERTS FOR TIME SERIES: DISCOVERING REGIMES AND AVOIDING OVERFITTING

1995 ◽  
Vol 06 (04) ◽  
pp. 373-399 ◽  
Author(s):  
ANDREAS S. WEIGEND ◽  
MORGAN MANGEAS ◽  
ASHOK N. SRIVASTAVA
Keyword(s):  
Time Series ◽  
Real World ◽  
Markov Models ◽  
Multilayer Perceptrons ◽  
Time Step ◽  
Local Complexity ◽  
Previous State ◽  
Segmentation Task ◽  
Gating Network ◽  
Update Rules

In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes), and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes; (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state); and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are: (1) the gating network correctly discovers the different regimes of the process; (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes); and (3) there is less overfitting compared to single networks (homogeneous multilayer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.

Behavior of Collective Cooperation Yielded by Two Update Rules in Social Dilemmas: Combining Fermi and Moran Rules

2012 ◽  
Vol 58 (3) ◽  
pp. 343-348 ◽  
Author(s):  
Cheng-Yi Xia ◽  
Lei Wang ◽  
Juan Wang ◽  
Jin-Song Wang
Keyword(s):  
Social Dilemmas ◽  
Update Rules

scholarly journals A nonlinear relationship between prediction errors and learning rates in human reinforcement learning

2019 ◽  
Author(s):  
Erdem Pulcu
Keyword(s):  
Reinforcement Learning ◽  
Nonlinear Relationship ◽  
Prediction Errors ◽  
Learning Rates ◽  
The Face ◽  
In The Wild ◽  
Actual Outcome ◽  
Update Rules ◽  
Different Sources

AbstractWe are living in a dynamic world in which stochastic relationships between cues and outcome events create different sources of uncertainty1 (e.g. the fact that not all grey clouds bring rain). Living in an uncertain world continuously probes learning systems in the brain, guiding agents to make better decisions. This is a type of value-based decision-making which is very important for survival in the wild and long-term evolutionary fitness. Consequently, reinforcement learning (RL) models describing cognitive/computational processes underlying learning-based adaptations have been pivotal in behavioural2,3 and neural sciences4–6, as well as machine learning7,8. This paper demonstrates the suitability of novel update rules for RL, based on a nonlinear relationship between prediction errors (i.e. difference between the agent’s expectation and the actual outcome) and learning rates (i.e. a coefficient with which agents update their beliefs about the environment), that can account for learning-based adaptations in the face of environmental uncertainty. These models illustrate how learners can flexibly adapt to dynamically changing environments.

scholarly journals Ordering structured populations in multiplayer cooperation games

2015 ◽  
Author(s):  
Jorge Peña ◽  
Bin Wu ◽  
Arne Traulsen
Keyword(s):  
Population Structure ◽  
Spatial Structure ◽  
Structured Populations ◽  
Evolution Of Cooperation ◽  
Multiplayer Games ◽  
Spatial Games ◽  
Population Structures ◽  
Single Structure ◽  
Containment Order ◽  
Update Rules

AbstractSpatial structure greatly affects the evolution of cooperation. While in two-player games the condition for cooperation to evolve depends on a single structure coefficient, in multiplayer games the condition might depend on several structure coefficients, making it difficult to compare different population structures. We propose a solution to this issue by introducing two simple ways of ordering population structures: the containment order and the volume order. If population structure 𝒮1 is greater than population structure 𝒮2 in the containment or the volume order, then 𝒮1 can be considered a stronger promoter of cooperation. We provide conditions for establishing the containment order, give general results on the volume order, and illustrate our theory by comparing different models of spatial games and associated update rules. Our results hold for a large class of population structures and can be easily applied to specific cases once the structure coefficients have been calculated or estimated.

Dynamic Complexity of Parity Exists Queries

2021 ◽  
Vol Volume 17, Issue 4 ◽  
Author(s):  
Nils Vortmeier ◽  
Thomas Zeume
Keyword(s):  
Order Logic ◽  
First Order Logic ◽  
Dynamic Complexity ◽  
First Order ◽  
Update Rules ◽  
Simple Query

Given a graph whose nodes may be coloured red, the parity of the number of red nodes can easily be maintained with first-order update rules in the dynamic complexity framework DynFO of Patnaik and Immerman. Can this be generalised to other or even all queries that are definable in first-order logic extended by parity quantifiers? We consider the query that asks whether the number of nodes that have an edge to a red node is odd. Already this simple query of quantifier structure parity-exists is a major roadblock for dynamically capturing extensions of first-order logic. We show that this query cannot be maintained with quantifier-free first-order update rules, and that variants induce a hierarchy for such update rules with respect to the arity of the maintained auxiliary relations. Towards maintaining the query with full first-order update rules, it is shown that degree-restricted variants can be maintained.

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