Hierarchical Clustering of Ensemble Prediction Using LOOCV Predictable Horizon for Chaotic Time Series

Recently, we have presented a method of ensemble prediction of chaotic time series. The method employs strong learners capable of making predictions with small error, where usual ensemble mean does not work well owing to the long term unpredictability of chaotic time series. Thus, we have developed a method to select a representative prediction from a set of plausible predictions by means of using LOOCV (leave-one-out cross-validation) measure to estimate predictable horizon. Although we have shown the effectiveness of the method, it sometimes fails to select the representative prediction with long predictable horizon. In order to cope with this problem, this paper presents a method to select multiple candidates of representative prediction by means of employing hierarchical K-means clustering with K = 2. From numerical experiments, we show the effectiveness of the method and an analysis of the property of LOOCV predictable horizon.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

Grading Fruits and Vegetables Using RGB-D Images and Convolutional Neural Network

This paper presents a method for grading fruits and vegetables by means of using RGB-D (RGB and depth) images and convolutional neural network (CNN). Here, we focus on grading according to the size of objects. First, the method transforms positions of pixels in RGB image so that the center of the object in 3D space is placed at the position equidistant from the focal point by means of using the corresponding depth image. Then, with the transformed RGB images involving equidistant objects, the method uses CNN for learning to classify the objects or fruits and vegetables in the images for grading according to the size, where the CNN is structured for achieving both size sensitivity for grading and shift invariance for reducing position error involved in images. By means of numerical experiments, we show the effectiveness and the analysis of the present method.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon

Recently, we have presented a method of probabilistic prediction of chaotic time series. The method employs learning machines involving strong learners capable of making predictions with desirably long predictable horizons, where, however, usual ensemble mean for making representative prediction is not effective when there are predictions with shorter predictable horizons. Thus, the method selects a representative prediction from the predictions generated by a number of learning machines involving strong learners as follows: first, it obtains plausible predictions holding large similarity of attractors with the training time series and then selects the representative prediction with the largest predictable horizon estimated via LOOCV (leave-one-out cross-validation). The method is also capable of providing average and/or safe estimation of predictable horizon of the representative prediction. We have used CAN2s (competitive associative nets) for learning piecewise linear approximation of nonlinear function as strong learners in our previous study, and this paper employs bagging (bootstrap aggregating) to improve the performance, which enables us to analyze the validity and the effectiveness of the method

Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification

So far, we have presented amethod for text-promptedmultistep speaker verification using GEBI (Gibbs-distribution based extended Bayesian inference) for reducing single-step verification error, where we use thresholds for acceptance and rejection but the tuning is not so easy and affects the performance of verification. To solve the problem of thresholds, this paper presents a method of probabilistic prediction in multiclass classification for solving verification problem.We also present loss functions for evaluating the performance of probabilistic prediction. By means of numerical experiments using recorded real speech data, we examine the properties of the present method using GEBI and BI (Bayesian inverence) and show the effectiveness and the risk of probability loss in the present method.22nd International Conference on Neural Information Processing, ICONIP 2015, November 9-12, 2015, Istanbul, Turke

FIGURE 4 in Exobasidium siroboe sp. nov. (Exobasidiaceae) causing Exobasidium fruit deformation on Symplocos myrtacea in Japan

FIGURE 4. Symptoms of Exobasidium fruit deformation of Symplocos myrtacea by Exobasidium siroboe. A. Fruit deformation covered with white hymenia observed in July 2005 in Kagoshima Prefecture. B. Appearance of fruit deformation (center and right) and healthy (unaffected) fruit. Development of fruit deformation was not necessarily uniform. Parts of infected fruits apparently looked healthy (arrow). C. Example of symptom development of Exobasidium fruit deformation (TSH-B 0088). H: healthy fruit, IN: infected fruit, IN-A: swollen infected fruit, IN-B: decayed swollen infected fruit. D. Vertical section of infected fruit (TSH-B 0088). Asterisk indicates thick mesocarp and arrow indicates aborted seed in the cavity. H: healthy fruit, IN: infected fruit, INS: vertical section of infected fruit. Photographed by Hideyuki Nagao.Published as part of Nagao, Hideyuki, Kurogi, Shuichi & Ogawa, Seiji, 2023, Exobasidium siroboe sp. nov. (Exobasidiaceae) causing Exobasidium fruit deformation on Symplocos myrtacea in Japan, pp. 219-224 in Phytotaxa 579 (3) on page 223, DOI: 10.11646/phytotaxa.579.3.7, http://zenodo.org/record/755041

FIGURE 5 in Exobasidium siroboe sp. nov. (Exobasidiaceae) causing Exobasidium fruit deformation on Symplocos myrtacea in Japan

FIGURE 5. Quartet puzzling consensus ML tree of the nuclear internal transcribed spacer (ITS) regions -the large-subunit ribosomal DNA (LrDNA) concatenated data of Exobasidium. Support value (left) and Bootstrap value (right) are presented on the shoulders of branches. Values greater than 50% are indicated. Isolates used in the phylogenetic tree are shown by the number in the NIAS Gene Bank. The accession nos. for LrDNA and ITS are described below: E. otanianum MAFF238611 (AB177576/AB180343), E. japonicum MAFF238591 (AB177568/AB180326), E. woronichinii MAFF238666 (AB177578/AB180360), E. shiraianum MAFF238603 (AB177584/AB180337), E. miyabei MAFF238595 (AB177579/AB180330), E. kishianum MAFF238623 (AB177577/AB180353), E. camelliae MAFF238578 (AB176712/AB180317), E. reticulatum MAFF239442 (AB180381/AB180377), E. symploci-japonicae var. symploci-japonicae MAFF238605 (AB176711/AB180339) and MAFF238811 (AB178255/AB180678), E. symploci-japonicae var. carpogenum MAFF238620 (AB177559/AB180351), Laurobasidium hachijoense MAFF238665 (AB177562/AB180359), and Ustilago maydis (L20287/ AY854090). Information about E. siroboe is given in the text.Published as part of Nagao, Hideyuki, Kurogi, Shuichi & Ogawa, Seiji, 2023, Exobasidium siroboe sp. nov. (Exobasidiaceae) causing Exobasidium fruit deformation on Symplocos myrtacea in Japan, pp. 219-224 in Phytotaxa 579 (3) on page 223, DOI: 10.11646/phytotaxa.579.3.7, http://zenodo.org/record/755041

FIGURE 1 in Exobasidium siroboe sp. nov. (Exobasidiaceae) causing Exobasidium fruit deformation on Symplocos myrtacea in Japan

FIGURE 1. Basidia and basidiospores of Exobasidium siroboe on infected fruit of Symplocos myrtacea. Basidia (A1, A2, A3), basidiospores (B) and germinated basidiospores (C) collected in Kagoshima Prefecture (INM-2-17577-055270). Scale bar: 3 µm. Illustrated by Hideyuki Nagao.Published as part of Nagao, Hideyuki, Kurogi, Shuichi & Ogawa, Seiji, 2023, Exobasidium siroboe sp. nov. (Exobasidiaceae) causing Exobasidium fruit deformation on Symplocos myrtacea in Japan, pp. 219-224 in Phytotaxa 579 (3) on page 220, DOI: 10.11646/phytotaxa.579.3.7, http://zenodo.org/record/755041

Time series prediction of the CATS benchmark using Fourier bandpass filters and competitive associative nets

An approach to time series prediction of the CATS benchmark (for competition on artificial time series) is presented, where we use Fourier bandpass filters and competitive associative nets (CAN2s). Since one of the difficulties of this prediction is that the given time series does not seem to involve sufficient number of data for obtaining the underlying dynamics of the time series to reproduce low frequency components, we apply the CAN2 only for learning high frequency components extracted via Fourier bandpass filters with trial parameter values of the upper and lower cutoff frequencies and the missing last value of the given time series. Supposing that the optimal values among the trial values will give the best prediction performance for high frequency components, we can identify such optimal values via a certain reasonable validation method, with which we predict the missing high frequency components, and then we obtain the missing data to be predicted via adding high and low frequency components