چکیده شناسایی رقومی خاک ها بهعنوان ابزاری برای ایجاد اطلاعات مکانی خاک، راه حل هایی برای نیاز رو به افزایش نقشه های خاک با تفکیک مکانی بالا را تأمین می کند. بنابراین، باید روش های جدید بهمنظور بهدست آوردن اطلاعات مکانی خاک با تفکیک مکانی بالا توسعه پیدا کند. به همین منظور مطالعه ای جهت پیش بینی کلاس های خاک با استفاده از مدل های رگرسیونی در منطقه زرند کرمان طراحی گردید. در این مطالعه، مدل های ر...

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also b...

Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine Learning Studio tool for rapid assessment of multiple types of regression mo...

In this paper, we study the use of boosted weak classifiers selected with AdaBoost algorithm in object detection. Our work is motivated by the good performance of AdaBoost in selecting discriminative features and the effectiveness of Classification and Regression Tree (CART) compared with other classification methods. First, we study the cascaded structure of the boosted weak classifier detecto...

The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target lin...

Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit models of the ecological phenomenon of interest and the process by which it is observed. However, existing latent variable methods rely on handformulated parametric models, which are expensive to design and require extensiv...

Ensemble learning methods have received remarkable attention in the recent years and led to considerable advancement in the performance of the regression and classification problems. Bagging and boosting are among the most popular ensemble learning techniques proposed to reduce the prediction error of learning machines. In this study, bagging and gradient boosting algorithms are incorporated in...