1,040 research outputs found
The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
Genetic Algorithms: Genesis of Stock Evaluation
The uncertainty of predicting stock prices emanates pre-eminent concerns around the functionality of the stock market. The possibility of utilising Genetic Algorithms to forecast the momentum of stock price has been previously explored by many optimisation models that have subsequently addressed much of the scepticism. In this paper the author proposes a methodology based on Genetic Algorithms and individual data maximum likelihood estimation using logit model arguing that forecasting discrepancy can be rationalised by combined approximation of both the approaches. Thus this paper offers a methodological overture to further investigate the anomalies surrounding stock market. In the main, this paper attempts to provide a temporal dimension of the methods transposed on recurrent series of data over a fixed window conjecturereGenetic Algorithms, Individual Maximum Likelihood Estimation, Stock Price
Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis
In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP)
to functional inputs. We show that fundamental results for classical MLP can be
extended to functional MLP. We obtain universal approximation results that show
the expressive power of functional MLP is comparable to that of numerical MLP.
We obtain consistency results which imply that the estimation of optimal
parameters for functional MLP is statistically well defined. We finally show on
simulated and real world data that the proposed model performs in a very
satisfactory way.Comment: http://www.sciencedirect.com/science/journal/0893608
Using a multi-objective genetic algorithm for SVM construction
Support Vector Machines are kernel machines useful for classification and regression problems. In this paper, they are used for non-linear regression of environmental data. From a structural point of view, Support Vector Machines are particular Artificial Neural Networks and their training paradigm has some positive implications. In fact, the original training approach is useful to overcome the curse of dimensionality and too strict assumptions on statistics of the errors in data. Support Vector Machines and Radial Basis Function Regularised Networks are presented within a common structural framework for non-linear regression in order to emphasise the training strategy for support vector machines and to better explain the multi-objective approach in support vector machines' construction.
A support vector machine's performance depends on the kernel parameter, input selection and ε-tube optimal dimension. These will be used as decision variables for the evolutionary strategy based on a Genetic Algorithm, which exhibits the number of support vectors, for the capacity of machine, and the fitness to a validation subset, for the model accuracy in mapping the underlying physical phenomena, as objective functions. The strategy is tested on a case study dealing with groundwater modelling, based on time series (past measured rainfalls and levels) for level predictions at variable time horizons
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