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Text-based sentiment analysis is a growing research field in affective computing, driven by both commercial applications and academic interest. Continuous dimensional representations, such as valence-arousal (VA) space, can represent the affective state more precisely than discrete effective representations. In building dimensional sentiment applications, affective lexicons with valence-arousal ratings...
Genome-wide association (GWA) studies form an important category of research studies in personalized medicine which discuss on associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits. Considering the fast growing rate of GWA studies, automatic extraction of SNP-Traits associations from text is a highly demanding task. In this research, first an SNP-Trait association corpus...
In this paper, a new nonlinear subspace learning technique for class-specific data representation based on an optimized class representation is described. An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step. This approach is tested on human face and action recognition problems,...
The bag-of-audio-words approach has been widely used for audio event recognition. In these models, a local feature of an audio signal is matched to a code word according to a learned codebook. The signal is then represented by frequencies of the matched code words on the whole signal. We present in this paper an improved model based on the idea of audio phrases which are sequences of multiple audio...
Auroras are beautiful phenomena and attract many people. However, its physical model still remains a subject of dispute because it is caused by the interaction of diverse areas, such as solar wind, magnetosphere, and ionosphere, and it is difficult to simultaneously obtain data in such wide areas. This paper is devoted to forecasting the onset of brightening of auroras followed by poleward expansion,...
In this work, based on least squares support vector machine regression, a model that characterizes the relationship between constituents of Baikal skullcap root and therapeutic index of anti-respiratory syncytial virus was established. The computational simulation showed that this model fits well with the experimental data, and validation experimental results also supported the theoretical predictions.
In this paper, we describe a supervised subspace learning method that combines Extreme Learning methods and Bayesian learning. We approach the standard Extreme Learning Machine algorithm from a probabilistic point of view. Subsequently and we devise a method for the calculation of the network target vectors for Extreme Learning Machine-based neural network training that is based on a Bayesian model...
The development of a model classification intrusion detection using Weighted Extreme Learning Machine was examined with KDD'99 data set ad 4 types of main attack : Denial of Service Attack (DoS), User to Root Attack (U2R), Remote to Local Attack (R2L), and Probing Attack, when comparing the effectiveness of working process of the method presented to SVM+GA[6] and ELM, found that weighted technique...
As a new kind of social media, query log gains mass size of users and data. It's easy for people to post on query log. Also, the posts spread fast and can be easily seen by many other users. For the reasons above, users post various and large number contents on query log. Among these posts, we find numerous posts that express authors purchase wish for a certain product, in other words, consumption...
In this paper, we present an efficient semantic segmentation framework for indoor scenes operating on 3D point clouds. We use the results of a Random Forest Classifier to initialize the unary potentials of a densely interconnected Conditional Random Field, for which we learn the parameters for the pairwise potentials from training data. These potentials capture and model common spatial relations between...
In this paper, an approach for speeding up a kernel based nonlinear state estimator is proposed. The kernel based observer, which we are going to speed up in this paper, is one of the state estimator which employs a non-parametric structure. Although it shows high precision for nonlinear estimation due to its nonlinear nature, large amount of calculation makes it rather slow. In this paper, we propose...
Imbalanced data classification, which is a common and important problem in various fields related to the detection of anomaly, failure, and risk, has been studied intensively. Conventional methods are based on sampling, misclassification costs, or ensemble of classifiers, and many of them are heuristic and task dependent. Aiming at a higher classification performance with the solution of such problems,...
In many positioning control systems, the end of the positioning phase must be determined correctly in order to initiate the start of the next task. A simple way of determining the end of the positioning phase is to wait for several sampling periods in order to confirm that the output does not exceed the error bound. This makes the unnecessary wait inevitable. This paper proposes a method to determine...
In imbalanced learning, most standard classification algorithms usually fail to properly represent data distribution and provide unfavorable classification performance. More specifically, the decision rule of minority class is usually weaker than majority class, leading to many misclassification of expensive minority class data. Motivated by our previous work ADASYN [1], this paper presents a novel...
Mobile network failures have occurred many times in recent years. Some network failures become “silent” failures that mobile carriers cannot detect because of incomplete rules concerning failure detection by the network operating system. However, the increasing number of services and devices, and the increasing complexity of the network make it hard to generate rules that cover all network failures...
In this paper, we propose a novel regression method that can incorporate both positive and negative training data into a single regression framework. In detail, a leveraged kernel function for non-stationary Gaussian process regression is proposed. With this new kernel function, we can vary the correlation betwen two inputs in both positive and negative directions by adjusting leverage parameters...
Many modern face verification algorithms use a small set of reference templates to save memory and computational resources. However, both the reference templates and the combination of the corresponding matching scores are heuristically chosen. In this paper, we propose a well-principled approach, named sparse support faces, that can outperform state-of-the-art methods both in terms of recognition...
It is commonly agreed that the success of support vector machines (SVMs), is highly dependent on the choice of particular similarity functions referred to as kernels. The latter are usually handcrafted or designed using appropriate optimization schemes. Multiple kernel learning (MKL) is one possible scheme that designs kernels as sparse or convex linear combinations of existing elementary functions...
Theoretical validity of empirical error minimization in multiple kernel regressors is discussed in this paper. Generalization error of a kernel machine is usually evaluated by the induced norm of the difference between an unknown true function and an estimated one in an appropriate reproducing kernel Hilbert space. It is well known that empirical error minimization also achieves the minimum generalization...
In this paper we propose an Iterative Re-Weighted Least Square procedure in order to solve the Support Vector Machines for regression and function estimation. Furthermore, we include a new algorithm to train Support Vector Machines, covering both the proposed approach instead of the quadratic programming part and the most advanced methods to deal with large training data sets. Finally, the performance...
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