Memprediksi laju penguapan memiliki manfaat yang luas dalam berbagai aplikasi seperti manajemen sumber daya air, pertanian, dan lingkungan hidup. Namun untuk mendapatkan data lengkap akurat mempelajari tantangan tersendiri. Selain itu, rendahnya tingkat linieritas antara faktor meteorologi lainnya di wilayah tropis dapat menyebabkan hasil prediksi bervariasi. Tujuan dari penelitian ini adalah m...

We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm overcomes some of the computational difficulties related to batch methods for kernel regression. It is non-iterative, and requires only a single pass over the data. It is ...

In this paper, we present an approach for regression-based feature selection in human activity recognition. Due to high dimensional features in human activity recognition, the model may have over-fitting and can’t learn parameters well. Moreover, the features are redundant or irrelevant. The goal is to select important discriminating features to recognize the human activities in videos. R-Squar...

The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes. The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling. In th...

Distance measures like the Euclidean distance have been the most widely used to measure similarities between feature vectors in the content-based image retrieval (CBIR) systems. However, in these similarity measures no assumption is made about the probability distributions and the local relevances of the feature vectors. Therefore, irrelevant features might hurt retrieval performance. Probabili...

The paper deals with the concept of relevance learning in learning vector quantization. Recent approaches are considered: the generalized learning vector quantization as well as the soft learning vector quantization. It is shown that relevance learning can be included in both methods obtaining similar structured learning rules for prototype learning as well as relevance factor adaptation. We sh...

In response to the problems of slow running speed and high error rates traditional flight conflict detection algorithms, in this paper, we propose a algorithm based on use relevance vector machine. A set symmetrical historical data was used as training model, SMOTE resampling method optimize set. We obtained relatively trained it with machine, improving kernels through an intelligent algorithm....

Relevance vector machine (RVM) is a popular sparse Bayesian learning model typically used for prediction. Recently it has been shown that improper priors assumed on multiple penalty parameters in RVM may lead to an posterior. Currently the literature, sufficient conditions posterior propriety of do not allow over parameters. In this article, we propose single relevance (SPRVM) which are replace...