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41.
Renato Bruni 《Annals of Operations Research》2007,150(1):79-92
The paper is concerned with the problem of binary classification of data records, given an already classified training set
of records. Among the various approaches to the problem, the methodology of the logical analysis of data (LAD) is considered.
Such approach is based on discrete mathematics, with special emphasis on Boolean functions. With respect to the standard LAD
procedure, enhancements based on probability considerations are presented. In particular, the problem of the selection of
the optimal support set is formulated as a weighted set covering problem. Testable statistical hypothesis are used. Accuracy
of the modified LAD procedure is compared to that of the standard LAD procedure on datasets of the UCI repository. Encouraging
results are obtained and discussed. 相似文献
42.
Francisco Marco-Serrano 《Annals of Operations Research》2006,145(1):167-181
Managerial efficiency within the performing arts programming can be understood as the technical efficiency of transforming the resources cultural managers have available into a determined cultural output. Through this explanation different conceptions on the finished performance product it leads us to select two different output variables (number of performances, and number of attendances). In this way, three different models are considered regarding those conceptual points of view. Data on the Circuït Teatral Valencià, a Spanish regional theatres network, is used to develop empirically the concept of Managerial Efficiency and set up a framework to allow us to monitor it. 相似文献
43.
主要作为电子高科技的重点领域,专用实时设备无论类别和型号,抑或数量和质量,均与日俱增,令人目不暇接。它们的高效管理与灵活加接,是任何计算机专用实时系统不可或缺的基本功能,其驱动模块随之成为任何专用实时系统无法回避的设计难点。本文介绍我们自行研制的专用实时设备驱动程序交互式生成器RTDG的设计与实现。 相似文献
44.
I. Esteban-Díez 《Analytica chimica acta》2006,555(1):84-95
Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods. 相似文献
45.
Hong Tao Gao Dong Mei Dai Tong Hua Li 《中国化学快报》2007,18(4):495-498
Non-negative matrix factorization(NMF)is a technique for dimensionality reduction by placing non-negativity constraints onthe matrix.Based on the PARAFAC model,NMF was extended for three-dimension data decomposition.The three-dimension non-negative matrix factorization(NMF3)algorithm,which was concise and easy to implement,was given in this paper.The NMF3algorithm implementation was based on elements but not on vectors.It could decompose a data array directly without unfolding,which was not similar to that the traditional algorithms do.It has been applied to the simulated data array decomposition andobtained reasonable results.It showed that NMF3 could be introduced for curve resolution in chemometrics. 相似文献
46.
Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site. 相似文献
47.
48.
M. F. Gonnord I. Ignatiadis A. Jaulmes C. Vidal-Madjar 《Journal of separation science》1987,10(7):392-397
The performances of some numerical methods to improve the signal to noise ratio are compared and applied to enhance noisy signals obtained in gas chromatography with capillary columns and a flame Ionization detector. Several methods have been considered: cutoffs In the Fourier transform of the recorded signal; real time numerical filtering; theoretical model curve fitting; and the correlation of a chromatogram recorded from a pseudorandomly injected sample with the pseudorandom injection function. Numerical real time filtering is shown to be the most convenient method when the main periodic component of the noise has been determined by Fourier analysis. 相似文献
49.
Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics. 相似文献
50.
Existing point cloud classification researches are usually conducted on datasets with complete structure and clear semantics. However, in real point cloud scenes, the occlusion and truncation may destroy the completeness of objects affecting the classification performance. To solve this problem, we propose an incomplete point cloud classification network (IPC-Net) with data augmentation and similarity measurement. The proposed network learns the feature representation of incomplete point clouds and the semantic differences compared to the complete ones for classification. Specifically, IPC-Net adopts a random erasing-based data augmentation to deal with incomplete point clouds. IPC-Net also introduces an auxiliary loss function weighted by attention scores to measure the similarity between the incomplete and the complete point clouds. Extensive experiments verify that IPC-Net has the ability to classify incomplete point clouds and significantly improves the robustness of point cloud classification under different completeness. 相似文献