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In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral...
In this paper, we focus on the classification of lidar point cloud data acquired via mobile laser scanning, whereby the classification relies on a context model based on a Conditional Random Field (CRF). We present two approximate inference algorithms based on belief propagation, as well as a graph-cut-based approach not yet applied in this context. To demonstrate the performance of our approach,...
This study aims at evaluating two classes of methods to discriminate 13 peatland vegetation types using reflectance data from hyperspectral in situ measurements. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. We suppose that specific biophysical properties/components may help discriminating vegetation types applying supervised classification...
Using monthly as well as annual statistics, we investigate the potentials of synergetic utilization of multispectral and C-band SAR data for the classification of a study site in the central Brazilian state of Mato Grosso. We aim at the classification of five tropical land cover classes (primary forests, secondary vegetation, pasture, agricultural, water), and highlight the potentials of standalone...
Classification is one of the most important applications and also key technology of Polarimetric SAR image interpretation, which mainly includes feature extraction and optimization of classifiers. For high resolution Polarimetric SAR images, the fine description and accurate classification becomes increasingly complex and difficult with a single feature or classifier. Thus, the selective ensemble...
In this paper, a novel iterative clustering based active learning (ICAL) method for hyperspectral image classification is proposed. On the one hand, the extreme learning machine is combined with the Markov random field (ELM-MRF) for label assignment, to exploit both spectral and spatial information to boost classification result. On the other hand, an iterative clustering based sample selection strategy...
In this paper, a new subpixel mapping approach for hyperspectral image is proposed, using a spatial-spectral endmember dictionary with collaborative representation (CR). Different from the classic approaches, the proposed approach employ several spatially closest training samples as the endmembers used for the representation of each mixed pixel, instead of the entire training set. Furthermore, the...
In this article the structure and the features of the clinical statistical data processing to solve the diagnostic problems have been reviewed. The system software solution example is drawn on the basis of the neural network. The system serves for monitoring the user's state in real time with the help of Android device and web-service.
A decision tree is an important classification technique in data mining classification. Decision trees have proved to be valuable tools for the classification, description, and generalization of data. J48 is a decision tree algorithm which is used to create classification model. J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. In this paper, we present...
The aim of this work is to explore the usefulness of face semantic segmentation for head pose estimation. We implement a multi-class face segmentation algorithm and we train a model for each considered pose. Given a new test image, the probabilities associated to face parts by the different models are used as the only information for estimating the head orientation. A simple algorithm is proposed...
Bagging is a popular method used to increase the accuracy of classification, by training a set of classifiers on slightly different datasets and aggregating their output by voting. Usually, the majority voting is used for this purpose, or the plurality voting, when the problem has multiple class values. In this study, we analyze the influence of several voting methods on the performance of two classification...
The aim of the paper was to apply MapReduce paradigm to the algorithm SplitBal which classifies imbalanced datasets and perform the evaluation of results for different parameters. Parallelization of time consuming operations allows to classify larger datasets, in perspective Big Data.
The paper focuses on using stacking and rotation-based technique to improve performance and generalization ability of the machine learning classification with data reduction. The aim of data reduction technique is decreasing the quantity of information required to learn a high quality classifiers, especially when the data are huge. The paper shows that merging both stacking and rotation-based ensemble...
Dempster-Shafer theory (DST) is an important theory for information fusion. However, in DST how to determinate the basic belief assignment (BBA) is still an open issue. The interval number based BBA determination method is simple and effective, where the features of different classes' samples are modeled using the interval numbers, i.e., an interval number model is constructed for each focal element...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
Detecting events on time series data generated by sensors has received a great amount of attention with increasingly deployment of variable sensors. In this paper, we propose a novel framework for classifying events upon sensors data called BEC. Given long raw time series and event labels on fuzzy time points, BEC extracts burst-based features to represent the events. There are mainly two important...
In order to satisfy the higher precision of indoor location-based service (ILBS), scholars have explored a great deal of algorithms based on Wi-Fi, ultrasonic, RFID or infrared, but all of which need additional device settings for transmitting and receiving signals before implementing location recognition. This paper proposed an idea that how to conveniently find the optimal feature or composite features...
In this contribution, we consider the classification of time-series and similar functional data which can be represented in complex Fourier coefficient space. We apply versions of Learning Vector Quantization (LVQ) which are suitable for complex-valued data, based on the so-called Wirtinger calculus. It makes possible the formulation of gradient based update rules in the framework of cost-function...
This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather...
Yield estimation is becoming a challenging task for circuits that are replicated in millions of instances on a large design (High Replication Circuits, HRC) such as SRAMs and flip flops. This is because a rare event in a circuit cell may have a large impact on the system yield. To achieve high yield in HRC, the failure probability of the individual cell is requested to be very small. Thus the number...
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