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In this paper, we propose a novel kernel learning scheme for acoustic scene classification using multiple short-term observations. The method takes inspiration from the recent result of psychological research — "Humans use summary statistics to perceive auditory sequences" we endeavor to devise computational framework imitating such important auditory mechanism for acoustic scene parsing...
Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data. However, for trivial monotonic signals, they struggle to perform accurate predictions and at the same time keep computational complexity within desired boundaries. This is because new observations are incorporated to the dictionary...
In kernel methods, temporal information on the data is commonly included by using time-delayed embeddings as inputs. Recently, an alternative formulation was proposed by defining a γ-filter explicitly in a reproducing kernel Hilbert space, giving rise to a complex model where multiple kernels operate on different temporal combinations of the input signal. In the original formulation, the kernels are...
High precision forecasting is a prerequisite and guarantee for the operation of grid-connected wind farms. Affected by various environmental factors, wind speed exhibits high fluctuations, autocorrelation and stochastic volatility. Therefore it remains great challenges for short-term wind speed forecasting. To capture its non-stationary property and its tendency, a forecasting model using support...
Timely and accurate traffic classification and application characterization are becoming increasingly important with many applications in wired and wireless networks, e.g., traffic engineering, security monitoring, and quality of service (QoS). In particular, Software Defined Networking (SDN) is a new networking paradigm that has great impact on future IP networks and 5G wireless networks. In SDN...
A fault diagnosis method was proposed based on Semi-supervised manifold learning and Transductive support vector machine (TSVM), to overcome scarcity of labeled training samples. Firstly, wavelet packet decomposition (WPD) was used to decompose vibration signals into several sub-bands. The fault features were extracted from the sub-bands to construct a high-dimensional fault feature set, and the improved...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
Machine Learning (ML) approaches are widelyused classification/regression methods for data mining applications. However, the time-consuming training process greatly limits the efficiency of ML approaches. We use the example of SVM (traditional ML algorithm) and DNN (state-of-the-art ML algorithm) to illustrate the idea in this paper. For SVM, a major performance bottleneck of current tools is that...
Ultrasound image is one of the modalities that is widely used to examine the abnormality of thyroid gland since it is relatively low-cost and safety. Fine needle aspiration biopsy (FNAB) is usually used by radiologists to determine the thyroid nodule whether malignant or benign. Commonly, malignancy of thyroid nodule determined based on shape feature. This research proposes a scheme for classifying...
In this paper, we explored the development of an anxiety detection (AnD) system using the respiratory signal as its input. Time and frequency domain statistical features derived from breath-to-breath (BB) interval series of respiratory signal is input to a support vector machine (SVM) backend classifier. We used data from normative population, individuals with anxiety disorders and regular meditators...
Essays in different text genres have different ideas and writing method. Prediction the text genres firstly will help get a better accuracy when predicting the success of literary or finding the beautiful words and sentences in the essay. And it will help set a different standard for different text genres when scoring the writing by computer. Words and structure can be effective in discriminating...
Based on the spectral data from SDSS, Kernel Support Vector Machines (K-SVM) is applied to classify quasars from other celestial body. Firstly, the basic theory of the SVM(Support Vector Machine) with relaxation factor and kernel function is introduced. Then, the main parameters are designed and selected. Finally, the method is applied to the classification and identification of the quasars. The classification...
In recent years due to increased competition between companies in the services sector, predict churn customer in order to retain customers is so important. The impact of brand loyalty and customer churn in an organization as well as the difficulty of attracting a new customer per lost customer is very painful for organizations. Obtaining a predictive model customer behaviour to plan for and deal with...
Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
Pedestrian detection is considered as an active area of research and the advent of autonomous vehicles for a smarter mobility has spearheaded the research in this field. In this paper, design of a real-time pedestrian detection system for autonomous vehicles is proposed and its performance is evaluated using images from standard datasets as well as realtime video input. The proposed system is designed...
With the fast development of various methods for image classification using the bag-of-features model, machines can efficiently classify images by image content. Spatial pyramid matching (SPM) for sparse coding to create the dictionary is a popular and very well performing approach for image classification. The linear SPM was proposed to take advantage of the speed of the linear Support Vector Machine...
Nowadays, fault detect and prediction is quite important for the purpose of ensuring the correct functioning of complex system; nevertheless, it is usually difficult to establish an exact mathematical model in analytical form for complex system, therefore, fault prediction of complex system always relays on the analysis of the observed chaotic time series. In order to enhance the validity and accuracy...
Twin support vector regression and its extensions have been widely applied in machine learning and data mining. However, most of them can not achieve the satisfactory performances when the noise is involved. To this end, this paper presents a weighted least squares twin support vector regression (WLSTSVR) which can reduce the influence of the noise on prediction accuracy by using the information of...
Due to the particularity of the aero-engine bearing and the limitation of the test conditions, it is difficult to get enough fault class sample data and the misclassification cost that misjudge fault sample to normal sample is higher than the opposite misjudgment, therefore the diagnosis of aero-engine bearing belongs to the typical small sample problem which is also unbalance. In order to solve this...
Zepeda and Pérez [41] have recently demonstrated the promise of the exemplar SVM (ESVM) as a feature encoder for image retrieval. This paper extends this approach in several directions: We first show that replacing the hinge loss by the square loss in the ESVM cost function significantly reduces encoding time with negligible effect on accuracy. We call this model square-loss exemplar machine,...
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