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In this paper, we propose multi-objective differential evolution (DE) based feature selection and ensemble learning techniques for biomedical entity extraction. The algorithm operates in two layers, first step of which concerns with the problem of automatic feature selection for a machine learning algorithm, namely Conditional Random Field (CRF). The solutions of the final best population provides...
We revisit a well-studied problem in the analysis of range data: surface normal estimation for a set of unorganized points. Surface normal estimation has been well-studied initially due to its theoretical appeal and more recently due to its many practical applications. The latter cover several aspects of range data analysis from plane or surface fitting to segmentation, object detection and scene...
We present a methodology, called communication-aware virtual infrastructures (COMAVI), for the concurrent migration of multiple Virtual Machines (VMs) in cloud computing infrastructures, which aims at the optimum use of the available computational and network resources, by capturing the interdependencies between the communicating VMs. This methodology uses multiple criteria for selecting the VMs that...
The paper is introducing the principles of a new global optimization strategy, Imperialistic Strategy (IS), applied to the Continuous Global Optimization Problem (CGOP). Inspired from existing multi-population strategies, like the Island Model (IM) approaches to parallel Evolutionary Algorithms (EA) and the Imperialistic Competitive Algorithm (ICA), the proposed IS method is considered an optimization...
Simultaneous pose and correspondence estimation problem is used to determine the pose of a 3D object from a single 2D image when corresponding relation is unknown between 3D object points and 2D image points. The problem arises in many areas of computer vision and some algorithms have been presented. However, all the state-of-art algorithms rely on appropriate initialization and the correct solution...
A conventional k out of n visual cryptographic scheme ((k, n)-VCS) encodes one secret image P into n random-looking transparencies (called shares) such that any group of k or more transparencies reveals P to our eyes when they are superimposed, while that of less than k ones cannot. Given one secret image P and one cover image C, we study (k, n)-VCS with meaningful shares in this paper where shares...
This research paper builds on existing knowledge in the field of parametric Linear Programming (pLP) and proposes a continuous mathematical model that considers a multi-period Quantity Flexibility (QF) contract between a car manufacturer (buyer) and external parts supplying company. The supplier periodically delivers parts to the car manufacturer as agreed in the contract. Due to the uncertainty of...
One of the fast similarity search techniques is a binary hashing method that transforms a real-valued vector into a binary code. The similarity between two binary codes is measured by their Hamming distance. In this method, a hash table is often used for realizing the constant time similarity search. The number of accesses to the hash table, however, increases when the number of bits becomes long...
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted...
Decision making of worker for appropriate task selection based on workflow is often changed by occurring interruption. Concerned workers of interrupted workflow must optimize workflow to improve consumed time of current tasks by changing engagement of assigned tasks. Although work efficiency is improved by supporting experienced worker for inexperienced worker, time for nurture of inexperienced worker...
In this paper, we propose a new method to learn the regression coefficient matrix for multiple-output regression, which is inspired by multi-task learning. We attempt to incorporate high-order structure information among the regression coefficients into the estimated process of regression coefficient matrix, which is of great importance for multiple-output regression. Meanwhile, we also intend to...
We propose a new filter methodology for feature selection using the concept of game theory whereby features are assimilated to players. In this game theoretical context, a strategy corresponds to a particular affinity between a group of features forming a cluster, and the payoff function is computed based on the weighted distance between a feature and a cluster. A zero-sum two-player game problem...
In this paper, the nonlinear optimal control problem is formulated as a multi-objective mathematical optimization problem. Harmony search (HS) algorithm is one of the new heuristic algorithms. The differential harmony search (DHS) optimization algorithm is introduced for the first time in solving the optimal power flow(OPF) solution. A case on optimal power flow problem in the IEEE 30 bus system is...
This paper proposes a transfer learning scheme for traffic pattern analysis where the transferred classifier could be trained with a small number of samples. First we make feature descriptors to represent the traffic trajectories so that they should be adequate to transfer and classify the traffic patterns. Then, we use support vector machine (SVM) to learn the feature descriptors of traffic trajectories...
We propose a new criterion for discriminative dimension reduction, Max-K-Min Distance Analysis (MKMDA). Given a data set with C classes, MKMDA maximizes the sum of the K minimum pair wise distance of these C classes on the selected one-dimensional subspace. The set of the possible one-dimensional subspace, for which the order of the projected class centroids is identical, define a convex region with...
For checking the optimality of the objective function, we introduce a lexicographic order relation to compare two arbitrary triangular fuzzy numbers. Based on the order relation, the multi-objective fully fuzzy linear programming problem is converted into a crisp multi-level multi-objective linear programming problem, which may be solved by using the Min operator step by step. A numerical example...
Recent research has demonstrated that sparse coding (or sparse representation) is a powerful tool for pattern classification. This paper presents a new unsupervised feature selection method, termed Sparse Representation Preserving Feature Selection (SRPFS), which aims at minimizing reconstruction residual based on sparse representation in the subspace of the selected features. A greedy algorithm and...
This paper proposes a new algorithm for image recognition, which consists of (i) modeling categories as a set of distinctive parts that are discovered automatically, (ii) aligning them across images while learning their visual model, and, finally (iii) encode images as sets of part descriptors. The so-obtained parts are free of any appearance constraint and are optimized to allow the distinction between...
Transfer learning aims to address the problem where we lack the labeled data for training in one domain while utilizing the sufficient training data from other relevant domains. The problem becomes even more challenging when there are no labeled data in the target domain to build the association between two domains, which is more common in real-world scenarios. In this paper, we tackle with the challenge...
This paper presents a new way to formulate and solve the distribution system optimal reconfiguration problem. The system equations representing the network topology, power flow equation and objective function equation are transformed into an artificial dynamic model formulated by using only differential equations, on the basis of an error function defined into a Lyapunov space domain. Possible inequality...
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