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Data mining is the process of extraction of relevant information from a collection of data. Mining of a particular information related to a concept is done on the basis of the feature of the data. The accessing of these features hence for data retrieval can be termed as the feature extraction mechanism. Different type of feature extraction methods are being used. The feature selection algorithm should...
Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience replay method is proposed that ensures that the distribution of the experiences used for training is between that of the policy and a uniform distribution. Through experiments on a magnetic manipulation task it is shown that the method reduces...
Person reidentification is a problem of recognizing a person across non-overlapping camera views. Pose variations, illumination conditions, low resolution images, and occlusion are the main challenges encountered in reidentification. Due to the uncontrolled environment in which the videos are captured, people could appear in different poses and due to which the appearance of a person could vary significantly...
As self driving cars become more ubiquitous, users would look for natural ways of informing the car AI about their personal choice of routes. This choice is not always dictated by straightforward logic such as shortest distance or shortest time, and can be influenced by hidden factors, such as comfort and familiarity. This paper presents a path learning algorithm for such applications, where from...
In this paper, we considere a 5G network in which the requests from users arrive in a sequential manner. A centralized spectrum manager (SM) is responsible for assigning the heterogeneous requests from users to different spectrum fragments. Unlike existing works which assumed the users access without bursty, we consider the more practical settings that the occupation or release of spectrum is stochastic...
Missing value imputation is a crucial and challenging research topic in data mining because the data in real life are often contains missing value. The incorrect way to handle missing value will lead major problem in data mining processing to produce a new knowledge. One technique to solve Missing value imputation is by using machine learning algorithm. In this paper, we will present a new approach...
New strategies based on cognitive radio are being discussed to make a more efficient use of the HF band. Multiple users transmit in this band with a worldwide coverage but having multiple collisions with other HF stations. The use of the Upper Confidence Bound (UCB) algorithm is proposed in this work to provide them with a dynamic spectrum access mitigating mutual interference. Based on reinforcement...
Supervised learning has been applied in image processing system for object recognition, inspection and measurement. However the teaching-learning mode of supervised learning is not practical in real application, because it is impossible to teach a system all possible samples in one time. Therefore, incremental learning is considered to be a promising solution which supports the iteration of teaching-learning...
The elasticity characteristic of cloud computing enables clients to acquire and release resources on demand. This characteristic reduces clients' cost by making them pay for the resources they actually have used. On the other hand, clients are obligated to maintain Service Level Agreement (SLA) with their users. One approach to deal with this cost-performance trade-off is employing an auto-scaling...
The web contains enormous amount of information. From that enormous information only small amount of that information is visible to users and a huge portion of the information is not visible to the users. This is because traditional search engines are not able to index or access all information. The information which can be retrieved by following hypertext links are accessed by such traditional search...
In this paper we present a SVM-based method for automatic quality control of a road database in urban areas. The road verification is carried out by comparing the database objects to high-resolution aerial imagery. The method is trimmed to produce reliable results even if the training data selection is partly non-epresentative. A reliability metric is assigned to the SVM decision that is based on...
Sudden Cardiac Death (SCD) is an unexpected death caused by loss of heart function when the electrical impulses fired from the ventricles become irregular. Most common SCDs are caused by cardiac arrhythmias and coronary heart disease. They are mainly due to Acute Myocardial Infarction (AMI), myocardial ischaemia and cardiac arrhythmia. This paper aims at automating the recognition of ST-segment deviations...
In a cloud computing environment, resources are shared among different clients. Intelligently managing and allocating resources among various clients is important for system providers, whose business model relies on managing the infrastructure resources in a cost-effective manner while satisfying the client service level agreements (SLAs). In this paper, we address the issue of how to intelligently...
Human detection is a key functionality to reach Human Robot/Computer Interaction. The human tracking is also a rapidly evolving area in computer and robot vision; it aims to explore and to follow human motion. We present in this article an intelligent system to learn human detection. The descriptors used in our system make up the combination of HOG and SIFT that capture salient features of humans...
Data mining concerns theories, methodologies, and in particular, computer systems for knowledge extraction or mining from large amounts of data. Association rule mining is a general purpose rule discovery scheme. It has been widely used for discovering rules in medical applications. The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various...
It has great significance to efficiently distinguish the type of the samples' data in the decision table after the discretization for the course of machine learning and data mining afterwards. This paper puts forward an annotation method of distinguishing the data type based on attributes importance and the samples entropy, and processed the simulation test using part of the UCI database which was...
Breast Cancer is one of the frequent and leading causes of mortality among woman, especially in developed countries. Woman within the age of 40-69 have more risk of breast cancer. Though breast cancer leads to death, early detection of breast cancer can increase the survival rate. Clustered Microcalcification (MC) in mammograms is the major indication for early detection of breast cancer. MC is quiet...
The capability to visually discern possible obstacles from the sky would be a valuable asset to a UAV for avoiding both other flying vehicles and static obstacles in its environment. The main contribution of this article is the presentation of a feasible approach to obstacle avoidance based on the segmentation of camera images into sky and non-sky regions. The approach is named the Sky Segmentation...
Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost,...
In addressing side information based face recognition scenario, a new Margin Emphasized Metric Learning (MEML) method is proposed. As an improvement of previous metric learning, MEML defines a new objective function for optimization, which adds more weights to sample pairs on the boundary thus hard to classify. To further improve face verification performance, MEML is applied to Gabor feature in a...
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