The rational evolution of optimization methods, from univariate to multivariate, is described together with selected examples of their application. A novel approach, parametric modulation, is then proposed which overcomes many limitations of the present methods. In this approach, each mobile phase, ...
Norioka T et al (2011) Optimization of the manufacturing process for oral formulations using multivariate statistical methods. J Pharm Innov 6(3):157–169NORIOKA,T.; KIKUCHI,S.; ONUKI,Y.; TAKAYAMA,K.; IMAI,K. Optimization of the manufacturing process for oral formulations using multivariate ...
Multivariate Methods 来自 IEEEXplore 喜欢 0 阅读量: 13 作者: E Alpaydin 摘要: This chapter contains sections titled: 5.1 Multivariate Data, 5.2 Parameter Estimation, 5.3 Estimation of Missing Values, 5.4 Multivariate Normal Distribution, 5.5 Multivariate Classification, 5.6 Tuning Complexity, 5.7 ...
An inference procedure on the edges is used in the graphs to effectively remove false‐positive edges, which are caused by the data deviating from normality. The techniques are compared using simulated multivariate t ‐distributed (heavy‐tailed) time series data and the best method is applied to...
Multivariate Analysis Philosophical Methods Psychological Methods Notes For convenience, in this paper, we use the language of preference for describing the order on the criteria scale and on the categories. Note that these orders can represent many other aspects but preferences, such as risk, vulnera...
As described above, variable selection is a critical step in multivariate calibration of the analysis of NIR spectra. For NIR spectral technique, multivariate calibration [52] is defined as, “A process for creating a model that relates sample properties ‘y’ to the intensities or absorbance ‘...
Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application
Multivariate quartic polynomial optimization problems, as a special case of the general polynomial optimization, have a lot of practical applications in real world and are proved to be NP-hard. In this paper, some necessary local optimality conditions and some necessary global optimality conditions for...
The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net, Naïve Bayes (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, and Random ...
Monotonic optimization is concerned with optimization problems dealing with multivariate monotonic functions and differences of monotonic functions. For the study of this class of problems a general framework (Tuy, 2000a) has been earlier developed where a key role was given to a separation property ...