Most of the recent classification methods require tuning of the hyper-parameters, such as the kernel function parameter and the regularization parameter. Cross-validation or the leave-one-out method is often used for the tuning, however their computational costs are much higher than that of obtaining a classifier. Quadratically constrained maximum a posteriori (QCMAP) classifiers, which are based on the Bayes classification rule, do not have the regularization parameter, and exhibit higher classification accuracy than support vector machine (SVM). In this paper, we propose a multiple kernel learning (MKL) for QCMAP to tune the kernel parameter automatically and improve the classification performance. By introducing MKL, QCMAP has no parameter to be tuned. Experiments show that the proposed classifier has comparable or higher classification performance than conventional MKL classifiers.

Overview of Improved Limb Atmospheric Spectrometer (ILAS) instrument design, band selection studies, and operation plan is described. The ILAS is a solar occultation instrument onboard ADEOS spacecraft with two grating spectrometers: one is for measurement for O3, HNO3, NO2, N2O, H2O, CH4 CFC11 and CFC12 in the infrared band (850-1610cm-1, 11.76µm-6.21m), and another is for aerosols, temperature and air density measurement in the visible band (753-784nm, O2 atmospheric A band). The ILAS will observe the ozone layer over high-latitudes (N55-70, S63-87) regions with a high vertical resolution (2km) for a period of 3 years after launch in 1996.