There are no algorithms that generally perform better or worse than random when looking at all possible data sets according to the no-free-lunch theorem. A specific forecasting method will hence naturally have differe...
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There are no algorithms that generally perform better or worse than random when looking at all possible data sets according to the no-free-lunch theorem. A specific forecasting method will hence naturally have different performances in different empirical studies. This makes it impossible to draw general conclusions, however, there will of course be specific problems for which one algorithm performs better than another in practice. Meta-learning exploits this fact by linking characteristics of the data set to the performances of methods, adapting the selection or combination of base methods to a specific problem. This contribution describes an approach using meta-learning for time series forecasting in the NN GC1 competition. In order to generate bigger and more reliable meta-data set, data of the past NN3 and NN5 competitions have been included. A pool of individual forecasting and combination models are combined using a ranking algorithm with weights being determined by past performance on similar series.
Numerous feature selection methods have been developed to identify informative genes from a large pool of genes that are not involved in the array experiments. However, the integrity of the reported genes is still unc...
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Numerous feature selection methods have been developed to identify informative genes from a large pool of genes that are not involved in the array experiments. However, the integrity of the reported genes is still uncertain due to the applications of various pre-processing techniques to the microarray data by these methods and a lack of standard validation procedures to validate the significance of the genes. In this paper, we developed a feature extraction framework based on the hybrid genetic algorithm (GA) and neural network (ANN) to extract informative genes from the raw (unprocessed) microarray data. This approach has showed its efficacy in extracting informative genes for microarray data.
We present a method that utilises dynamic molecular modelling technique to track the changes within complex social network. The users forming a social network are interpreted as large sets of interacting particles. Th...
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Greedy and Contention-based forwarding schemes perform data routing hop-by-hop, without discovering the end-to-end route to the destination. Accordingly, the neighboring node that satisfies specific criteria is select...
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The evolution of patient monitoring on general hospital wards is discussed. Patients on general wards are monitored according to the severity of their conditions, which can be subjective at best. A report by the Commi...
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The extraction of social groups from social networks existing among employees in the company, its customers or users of various computer systems became one of the research areas of growing importance. Once we have dis...
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Greedy and Contention-based forwarding schemes perform data routing hop-by-hop, without discovering the end-to-end route to the destination. Accordingly, the neighboring node that satisfies specific criteria is select...
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Greedy and Contention-based forwarding schemes perform data routing hop-by-hop, without discovering the end-to-end route to the destination. Accordingly, the neighboring node that satisfies specific criteria is selected as the next forwarder of the packet. Both schemes require the nodes participating in the selection process to be within the area that confronts the location of the destination. Therefore, the lifetime of links for such schemes is not only dependent on the transmission range, but also on the location parameters (position, speed and direction) of the sending node, the neighboring node and the destination. In this paper, we propose a new link lifetime prediction method for greedy and contention-based routing. The evaluation of the proposed method is conducted by the use of stability-based greedy routing algorithm, which selects the next hop node having the highest link lifetime.
A method for extraction of the multi-layered social network based on the data about human collaborative achievements, in particular scientific papers, is presented in the paper. The objects linking people form a hiera...
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This paper proposes a set of higher-order modified moments as alternative objective criteria for pitch extraction and explores the impact of the speech window length on pitch estimation error. To obtain the K~(th) ord...
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ISBN:
(纸本)9781424442959
This paper proposes a set of higher-order modified moments as alternative objective criteria for pitch extraction and explores the impact of the speech window length on pitch estimation error. To obtain the K~(th) order modified moment, each speech frame is split into a positive-valued signal and a negative-valued signal. The magnitudes of the K~(th) order moments for the positive and the negative valued signals are obtained and combined. The proposed objective criteria form a relatively sharp peak around the true pitch value compared to the correlation function. For calculation of errors, pitch reference ('ground truth') values are calculated from manually-corrected estimates of the periods obtained from laryngograph signals. The results obtained for the third order modified moment are compared with the results for correlation and magnitude difference criteria and the YIN method. The modified moments provide improved pitch accuracy with less occurrence of large errors (e.g. half or double pitch estimation errors).
This paper describes a new dynamic partial reconfiguration (DPR) design flow and environment for image processing algorithms. The functionality and design techniques are demonstrated through an efficient implementatio...
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