porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. nowadays, using intelligent techniques has become a popular method for porosity estimation. support vector machine (svm) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...

different approaches have been proposed for feature selection to obtain suitable features subset among all features. these methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. the objective functions are divided into two main groups: filter and wrapper methods.  in filter methods, features subsets are selected due to some measu...

segmentation and three‑dimensional (3d) visualization of teeth in dental computerized tomography (ct) images are of dentists’ requirements for both abnormalities diagnosis and the treatments such as dental implant and orthodontic planning. on the other hand, dental ct image segmentation is a difficult process because of the specific characteristics of the tooth’s structure. this paper presents ...

Land-use change models are used to explore the dynamics and drivers of land-use/landcover change and to inform policies affecting such change. A broad array of applications and modeling methods are available and each type has certain advantages and disadvantages depending on the objective of the research. This work presents an approach combining cellular automata (CA) model and supported vector...

Universum-support vector machine (U-SVM) is an elegant method for 2-class classification problem. It is systematically studied in this paper, including the existence and uniqueness of the primal problem as well as the relation between the solutions of primal problem and dual problem. We find that U-SVM uses 3-class classification approach to solve the 2-class classification problem. So we have ...

Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairn...

In this project, two different approaches to predict Bike Sharing Demand are studied. The first approach tries to predict the exact number of bikes that will be rented using Support Vector Machines (SVM). The second approach tries to classify the demand into 5 different levels from 1 (lowest) to 5 (highest) using Softmax Regression and Support Vector Machines. Index Terms –regression, classific...

We consider the problem of deriving class-size independent generalization bounds for some regularized discriminative multi-category classification methods. In particular, we obtain an expected generalization bound for a standard formulation of multi-category support vector machines. Based on the theoretical result, we argue that the formulation over-penalizes misclassification error, which in t...

From optimizing a car’s performance, encouraging safer driving to accurate insurance pricing, it is desirable to be able to identify a driver by his driving style. Using a car data supplied by MetroMile Inc., driving styles were analyzed and core features were extracted. Two learning models were investigated and evaluated:support vector machine (SVM) and multinomial logistic regression. High ac...

It is necessary to classify credit rating of small and medium-sized companies in Chinese growth enterprises board. We selected the data of 90 small and medium-sized companies, used fuzzy theory to calculate the qualitative variables, and reformulated support vector machine for ordinal regression method so that different input points can make different contributions to decide hyper plane, to ana...