Choosing the Tuning Parameter for Axial Data
Data Mining-Assisted Parameter Tuning of a Search Algorithm
... Keywords: data mining, differential ant-stigmergy algorithm, low-discrepancy sequences, meta-heuristic optimization, parameter tuning Received: December 1, 2014 The main purpose of this paper is to ...
8
Data-driven tuning of linear parameter varying precompensators
... scheduling parameter would be the position of the upper ...scheduling parameter, by using two independent sets of scheduling parameter measurements, the IV estimate will lead to consistent ...
20
A Survey on Automatic Parameter Tuning for Big Data Processing Systems
... different parameter settings or finding near-optimal parameter settings for various ...eter tuning, an experiment-driven method is the most approachable ...large parameter space, machine ...
37
Data-driven Precompensator Tuning for Linear Parameter Varying Systems
... a data-driven method is proposed for direct tuning of the parameters of precompensators for LTI ...measured data, rather than passing through a system modelling step and then minimising a criterion ...
6
Parameter tuning for numerical optimization algorithms
... by choosing the right input ...properly. Choosing algorithm parameters by hand can be inefficient and difficult as we often do not know the full extend of parameter ...efficient parameter ...
73
Parameter Tuning Using Gaussian Processes
... new data point ...initial data points for the initial Gaussian process model to be trained ...the parameter space, whose positions are specified by the user, using the base learning algorithm and the ...
124
From Parameter Tuning to Dynamic Heuristic Selection
... a data structure, similar to feature trees from software product lines ...on data, which is relevant to the selected algorithm ...filtered data we build a surrogate for the second level and predict ...
115
Statistical methods for parameter fine-tuning of metaheuristics
... of choosing a good set of parameter values, called the Parameter Setting Problem, and compares them from a methodological point of view focusing on the statistical procedures used so far by the ...
75
Multiscale Parameter Tuning of a Semantic Relatedness Algorithm
... RDF data, and on a particular set of weights assigned to the properties of RDF statements (types of arcs in the RDF ...The tuning process is controlled by a genetic algorithm using the Spearman’s rank ...
13
Case in Point. Voice Quality Parameter Tuning
... • Identified packet losses of 2-5% which is too high for voice We used an analyzer to identify the traffic types and traffic quantity for the links in question. We discovered that the main data traffic on links ...
6
Parameter tuning for the NFFT based fast Ewald summation
... described tuning for the calculation of the forces in the cloud wall system we already considered in the Example ...splitting parameter α and for the mesh size ...
39
Towards a Parameter Tuning Approach for a Map-Matching Algorithm
... Table III shows the regression coefficients of the significant factors of the analysis, and the odds ratios, e b , for main and 2-way interaction effects. An odds ratio larger than 1 indicates that the odds of the ...
6
A nested heuristic for parameter tuning in Support Vector Machines
... , choosing them from different kernel sets K j , [22], yielding K = { P R j=1 µ j K j : µ j ≥ 0, K j ∈ K j ∀j = 1, ...a tuning method to be able to adapt to ...
23
Using Paraphrases for Parameter Tuning in Statistical Machine Translation
... 5 Related Work The approach we have taken here arises from a typ- ical situation in NLP systems: the lack of sufficient data to accurately estimate a model based on super- vised training data. In a ...
8
Variable Selection and Parameter Tuning in High-Dimensional Prediction
... no tuning is achieved by V1, while V2 often yields higher error rates that can be compared to perform parameter ...the tuning parameter and the variable subset jointly from a multi-diensional ...
13
Self Hyper-parameter Tuning for Stream Recommendation Algorithms
... process data streams rely on human expertise for hyper-parameter ...Hyper-Parameter Tuning (SPT) algorithm to incremental recommendation ...recommendation data sets. The results show ...
12
Effectiveness of random search in SVM hyper-parameter tuning
... The Friedman statistical test with the Nemenyi post-hoc test and confidence level of 95% was applied for the exper imental results to compare the predictive performance of the optimization effect for all data ...
9
A nested heuristic for parameter tuning in Support Vector Machines
... 2: Data sets for the MKL tests ...the tuning problem when the kernel class is the K RBF , as defined by ...the parameter space Θ is low, namely, p = 2, we use the basic VNS given in Algorithm 1 for ...
24
Tuning Parameter Selection in L1 Regularized Logistic Regression
... validation data for testing the model, and the remaining k-1 subsets are used as training data to fit the ...validation data. Different values of the tuning parameter could result in ...
50
Enhancements of Sparse Clustering with Resampling and Considerations on Tuning Parameter
... The proposed method provides additional information on variable weights and clustering results through resampling. Bootstrapping was not used because bootstrapping resamples the data with replacement, which makes ...
57