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In multi-label image classification, each image is always associated with multiple labels and labels are usually correlated with each other. The intrinsic relation among labels can definitely contribute to classifier training. However, most previous studies on active learning for multi-label image classification purely mine label correlation based on observed label distribution. They ignore the mapping...
Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions...
Due to the simplicity of its implementation and the impressive performance, Extreme Learning Machine (ELM) has been widely used in applications of machine learning. However, there are two potential problems in ELM: 1) lack of an efficient method for minimizing error; 2) consideration of little inherent structural information about correlations among output components. To overcome those problems, this...
This paper introduces the application of attribute selection methods along with Bayes classifiers. The proposal has been evaluated in eleven binary and multi-class real data sets with a number of instances lower than a thousand and a number of attributes between eight and sixteen thousand. Among them, five data sets belong to the Bioinformatics area. Experiments show that, in general terms, the most...
This work considers the problem of fault localization in transparent optical networks. The aim is to localize single-link failures by utilizing statistical machine learning techniques trained on data that describe the network state upon current and past failure incidents. In particular, a Gaussian Process (GP) classifier is trained on historical data extracted from the examined network, with the goal...
Signal strength difference (SSD) is widely utilized as the feature for Wi-Fi fingerprint localization to tackle the heterogeneity between training device and target device, but the correlation between SSDs is largely ignored. In this paper, a novel scheme named LC-KDE is proposed. It utilizes local Fisher discriminant analysis (LFDA) to transform the original SSDs into weakly correlated features,...
We consider detection of spoofing relay attack in time-division duplex (TDD) multiple antenna systems where an adversary operating in a full-duplex mode, amplifies and forwards the training signal of the legitimate receiver. In TDD systems, the channel state information (CSI) can be acquired using reverse training. The spoofing relay attack contaminates the channel estimation phase. Consequently the...
The constructivist design of the pedagogical evaluation involves the analysis of the correlation between the curriculum and the assessment, which is particularly complex, involves the approach of the teaching-learning-evaluation process in a unitary way. The role of the school, defined by the formation for life, presupposes the constructivist learning of the students, which in fact is a constructed...
This work explores the true user QoE according to the users' preferences and behaviors when the users know that they are being observed and concern about their privacy. We propose a systematic privacy-aware QoE evaluation scheme based on the observable user data. Firstly, we translate the subjective privacy- aware QoE evaluation problem into the objective rational user analysis procedure. Then, a...
In database-driven spectrum sharing, despite the spectrum sharing policy given by a database, harmful interference can occur between a primary user (PU) and a secondary user (SU) due to the unexpected propagation paths. In a previous study, a primary exclusive region (PER) centered at a PU, wherein the SUs are forbidden to use the spectrum, has been proposed. However, the PER figure that efficiently...
Search results in technical forums are typically keyword based. The relevance of a link is usually gauged by closest content match. However, it has been shown in literature that users' click behavior is an integral part of deciding the relevance of a search result. Moreover, it is not just the number of clicks that matter, but time spent on a clicked link, order in which the links were clicked etc...
Cognitive Radio (CR) technology enables secondary users (SUs) to opportunistically access unused licensed spectrum owned by the primary users (PUs). Therefore, it can potentially significantly enhance communication capacity, and hence is very encouraging in aerospace communications and deserve thorough study. One of the key problems in cognitive aerospace communications is to determine spectrum availability...
Automatic Image Annotation (AIA) plays an important role in large-scaled intelligent image management and retrieval. Based on the correlation between image low-level features and high-level semantic concepts, images can be efficiently retrieved from large-scaled image dataset. Recently, many researchers leverage machine learning techniques to annotate images automatically. However, these methods still...
Tag recommendation has gained significant popularity for annotating various web-based resources including web services. Compared with other approaches, tag recommendation based on supervised learning models usually lead to good accuracy. However, a high-quality training data set is needed, which demands manual tagging efforts from domain experts. While we could leverage the tags of existing web services...
Categorical data exist in many domains, such as text data, gene sequences, or data from Census Bureau. While such data are easy for human interpretation, they cannot be directly used by many classification methods, such as support vector machines and others, which require underlying data to be represented in a numerical format. To date, most existing learning methods convert categorical data into...
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines...
The concept of Personal Data Storage (PDS) has recently emerged as an alternative and innovative way of managing personal data w.r.t. the service-centric one commonly used today. The PDS offers a unique logical repository, allowing individuals to collect, store, and give access to their data to third parties. The research on PDS has so far mainly focused on the enforcement mechanisms, that is, on...
Hyper-heuristics have emerged as an important strategy for combining the strengths of different heuristics into a single method. Although hyper-heuristics have been found to be successful in many scenarios, little attention has been paid to the subsets of heuristics that these methods manage and apply. In several cases, heuristics can interfere with each other and can be harmful for the search. Thus,...
Sparse representation based classification (SRC) has been introduced as a new algorithm for face recognition classification instead of the classical gradient-based algorithms. However, there are some limitations that influence the robustness properties in SRC. One of the most effective parameters that impacts the SRC performance is the directory of training samples. It should contain enough samples...
It is important to cut down the erection time and the operation guidance by studying the shield machine tool failure. In this paper, an ACO-BP algorithm based tool failure prediction model is established by utilizing the nonlinear mapping characteristics of neural network and mining data characteristics from the subway. According to the practical problems, the dependent variables and the independent...
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