Adaptive Designs of Experiments for Accurate
... optimization of expensive-to-compute numerical simulators or black-box functions 关1–3兴. A metamodel 共or surrogate model兲 is an approximation to system response constructed from its value at a limited number of selected input values, the design of experiments 共DoE兲. In many engineering problems, the ...
... optimization of expensive-to-compute numerical simulators or black-box functions 关1–3兴. A metamodel 共or surrogate model兲 is an approximation to system response constructed from its value at a limited number of selected input values, the design of experiments 共DoE兲. In many engineering problems, the ...
Probabilistic State-Dependent Grammars for Plan
... cost. For instance, in the expansions of Drive, there is a nonzero probability for passing even when the driver is at the intended exit. This probability captures the possibility that the driver fails to notice the exit, without requiring an explicit state variable for the driver’s belief. However, ...
... cost. For instance, in the expansions of Drive, there is a nonzero probability for passing even when the driver is at the intended exit. This probability captures the possibility that the driver fails to notice the exit, without requiring an explicit state variable for the driver’s belief. However, ...
Ensemble of Clustering Algorithms for Large Datasets
... One of the most effective approaches to clustering large datasets is the so-called grid-based approach [3], which involves transition from clustering of individual objects to clustering of the elements of the grid structure (cells) formed in an attribute space. This approach assumes that all objects ...
... One of the most effective approaches to clustering large datasets is the so-called grid-based approach [3], which involves transition from clustering of individual objects to clustering of the elements of the grid structure (cells) formed in an attribute space. This approach assumes that all objects ...
Application of data mining techniques for effort and
... its source in the popularization of data warehouses, business intelligence and knowledge management. Due to combining techniques originating from different science disciplines, such as statistics, mathematics, artificial intelligence or machine learning, the algorithms of data mining are characteriz ...
... its source in the popularization of data warehouses, business intelligence and knowledge management. Due to combining techniques originating from different science disciplines, such as statistics, mathematics, artificial intelligence or machine learning, the algorithms of data mining are characteriz ...
Using an evolutionary algorithm to search for control
... commercial applications and for research purposes, a UAV is defined as a flying unattended object that is remotely controlled or is autonomous. This type of equipment has gained such popularity that several countries have or are considering regulations or prohibitions on the use of such vehicles, e. ...
... commercial applications and for research purposes, a UAV is defined as a flying unattended object that is remotely controlled or is autonomous. This type of equipment has gained such popularity that several countries have or are considering regulations or prohibitions on the use of such vehicles, e. ...
Modelling Equidistant Frequency Permutation
... original variable into a set of Boolean variables corresponding to each original value. The problem constraints and symmetry breaking constraints are quite different on these two models. The three-dimensional model is able to break symmetry in three planes rather than two, and the two-dimensional mo ...
... original variable into a set of Boolean variables corresponding to each original value. The problem constraints and symmetry breaking constraints are quite different on these two models. The three-dimensional model is able to break symmetry in three planes rather than two, and the two-dimensional mo ...
WATER QUALITY ANALYSIS USING MACHINE LEARNING ALGORITHMS
... 7. Finally, the accuracy determined using equation for AC might not be an adequate performance measure when the number of negative cases is much greater than the number of positive cases. Suppose there are 1000 cases, 995 of which are negative cases and 5 of which are positive cases. If the system c ...
... 7. Finally, the accuracy determined using equation for AC might not be an adequate performance measure when the number of negative cases is much greater than the number of positive cases. Suppose there are 1000 cases, 995 of which are negative cases and 5 of which are positive cases. If the system c ...
Topics in 0-1 Data
... We describe a simple topic model, corresponding to a generative model of the observations. The model states that there is a number of topics in the data, and that the occurrences of topics are independent. Given that the topic occurs, the words belonging to that topic are also considered to be indep ...
... We describe a simple topic model, corresponding to a generative model of the observations. The model states that there is a number of topics in the data, and that the occurrences of topics are independent. Given that the topic occurs, the words belonging to that topic are also considered to be indep ...
Data Stream Clustering with Affinity Propagation
... data samples flowing in are categorized as discardable (outliers), or compressible (accounted for by the current model), or to be retained in the RAM buffer. Clustering, e.g., k-means, is iteratively applied, considering the sufficient statistics of compressed and discarded points, and the retained ...
... data samples flowing in are categorized as discardable (outliers), or compressible (accounted for by the current model), or to be retained in the RAM buffer. Clustering, e.g., k-means, is iteratively applied, considering the sufficient statistics of compressed and discarded points, and the retained ...
Aalborg Universitet Learning dynamic Bayesian networks with mixed variables Bøttcher, Susanne Gammelgaard
... in the network, given I t . A model like this is used in situations, where the observations do not follow the same model all the time, but can follow different models at different times. This gives for example the possibility to account for outliers. When a HMM is represented as a DBN, we assume tha ...
... in the network, given I t . A model like this is used in situations, where the observations do not follow the same model all the time, but can follow different models at different times. This gives for example the possibility to account for outliers. When a HMM is represented as a DBN, we assume tha ...
- White Rose Research Online
... This paper proposes a new U Bagging approach to boost the performance of the prediction model for imbalanced binary classification. This approach is different from previous approaches, which to the best of our knowledge all use identically sized bags (or nearly identical) to improve the performance ...
... This paper proposes a new U Bagging approach to boost the performance of the prediction model for imbalanced binary classification. This approach is different from previous approaches, which to the best of our knowledge all use identically sized bags (or nearly identical) to improve the performance ...
Distributed Adaptive Model Rules for Mining Big Data Streams
... is a list of features, a head with information to compute the prediction for those instance covered by the rule, and statistics of past instances to decide when and how to add a new feature to its body. The default rule is a rule with an empty body. For each incoming instance, AMRules searches the c ...
... is a list of features, a head with information to compute the prediction for those instance covered by the rule, and statistics of past instances to decide when and how to add a new feature to its body. The default rule is a rule with an empty body. For each incoming instance, AMRules searches the c ...
VISUAL ANALYTICS OF MANUFACTURING SIMULATION DATA
... A prerequisite for visual analytics is the clustering of the datasets under investigation. In conjunction with simulation data analysis, we propose to group individual simulation runs into clusters of similar output performance values. The first question to answer is therefore which output parameter ...
... A prerequisite for visual analytics is the clustering of the datasets under investigation. In conjunction with simulation data analysis, we propose to group individual simulation runs into clusters of similar output performance values. The first question to answer is therefore which output parameter ...