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Personality is the defining essence of an individual as it guides the way we think, act and interpret external stimuli. Classification of personality is important as it can serves as a framework in the job assignment task, particularly, in the high risk job including the Police Force. There are many attributes of individual traits but not all of them can be used to indicate individual personality...
MOOCs are Massive Open Online Courses, which are offered on web and have become a focal point for students preferring e-learning. Regardless of enormous enrollment of students in MOOCs, the amount of dropout students in these courses are too high. For the success of MOOCs, their dropout rates must decrease. As the proportion of continuing and dropout students in MOOCs varies considerably, the class...
In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig's diet (either nourished with feed or with acorns). For this purpose,...
Face recognition is an active and challenging task in pattern recognition and computer vision application. Sparse representation based classification has been verified to be powerful for face recognition. This paper proposes the metaface block sparse bayesian learning (MBSBL) based on the framework of sparse representation. The MBS-BL combines the metaface learning and block sparse bayesian learning...
In this paper, we present auto-encoder (AE), stacked auto-encoder (SAE) and sparse auto-encoder (SPAE) to classify gaits of horse riding for real riding coaching. The parameters of each auto-encoder are adjusted to compare the performance. The data is collected from 16 inertial sensors attached to a motion capture suit to construct a motion database. We build the motion features as the method of gaits...
This paper presents an extension of a comparative study of classifier architectures for automatic fault diagnosis, with a special emphasis on the Extreme Learning Machine (ELM), with and without kernel mapping. Besides the explanation of the ELM model, an attempt is made to find theoretical hints of the excellent generalization capabilities of this model, based on the findings of Cover about dichotomies...
Traditional stacked autoencoders have an equal number of encoders and decoders. However, while fine-tuned as a deep neural network the decoder portion is detached and never used. This begs the question: ‘do we need equal number of decoders and encoders’? In this study we explore asymmetric autoencoders — unequal number of encoders and decoders. We specifically address two tasks — 1. Classification...
A conformai predictor outputs prediction regions, for classification label sets. The key property of all conformai predictors is that they are valid, i.e., their error rate on novel data is bounded by a preset significance level. Thus, the key performance metric for evaluating conformal predictors is the size of the output prediction regions, where smaller (more informative) prediction regions are...
AdaBoost is one of the most popular algorithm for classification and has been successfully used for text classification, face detection and tracking. However noise sensitivity is regarded as a major disadvantage and previous works show that AdaBoost will be overfitting when dealing with the data sets with noisy data. To improve the noise tolerance of conventional AdaBoost, this paper proposed a preprocessing...
Traditional data processing methods for electronic noses (e-noses) need to use the whole response curves (including rise, steady and recovery phases) of sensor array, which leads to a long sampling time. The traditional methods also perform many steps such as signal pre-processing, feature generation/reduction, and classification, which increase the difficulty of selecting a suitable method for each...
With the appearance and development of the technology of malicious codes and other unknown threats, information security has drawn people's attention. In this paper, we investigate on behavior-based detection which is different from traditional static detection technology. Firstly, we discuss the procedure in detail, especially feature extraction and classification. Several machine learning methods...
Multi-temporal PolSAR data is suitable for crops classification and growth monitoring. It is still difficult to establish a classifier with good robustness and high generation over a long temporal acquisition duration. This work aims to provide a solution to this task by exploring benefits from both the target scattering mechanism interpretation and the advanced deep learning. A polarimetric-feature-driven...
Recurrent Neural Networks are widely used tools for the classification of variable length data. However, their training is generally a very time-consuming task, especially for problems with high dimensions. The classification method proposed in this paper aims to provide a fast and simple alternative. Extended Sequential Fuzzy Indexing Tables are following the principle behind lookup table classifiers...
Accurate and robust risk prediction methods are of critical importance in calculating insurance costs. In the present paper, we study the case of vehicle insurance and develop a computational intelligence based method for obtaining risk estimates based on the data provided by the client to the insurance company. The method is based on analyzing the contracts, processing the input data, applying classification,...
Extreme learning machine is an emerging neural network architecture that offers fast learning and generalization for multiple tasks. In this work, a scalable digital architecture for multi-classifier extreme learning machine (MT-ELM) is proposed. The proposed architecture performs multiple classification tasks without reconfiguring the network. The design is validated with MNIST dataset and it is...
Convolutional neural network (CNN) has been successfully used in many fields including image recognition. CNN is composed of input, convolution, pooling, hidden and output layers, and the weights and biases between layers except the ones between convolution and pooling layers are acquired by learning. In comparison to the conventional neural networks, the learning cost of CNN is higher, and the learning...
AdaBoost is a classic ensemble learning algorithm with good classifier performance. In the past, it mainly used weak classifier as base classifier, such as KNN. They are simple and easy to train, but the essence of the weak classifier, it is impossible to get very high classification accuracy. In order to improve the correct rate, this paper introduces the AdaBoost ensemble classifier based on convolutional...
The new advanced very high resolution (VHR) synthetic aperture radar (SAR) sensor is a kind of high-tech imaging radar developed rapidly in recent years, and it can get even less than 1 m high resolution SAR image. The feature of the VHR SAR image is different from the low or medium resolution SAR image and it contains more abundant information, so the traditional SAR image classification methods...
Augmentative and Alternative Communication (AAC) apps are apps that enable non-speech communicative forms. One class of AAC apps are speech-generating devices (SGDs), where icons/pictures are tapped to produce spoken words. These apps are widely used to support communication and language learning for individuals with disabilities such as autism spectrum disorder (ASD). Given that these apps are used...
Traditional block compressed sensing (BCS) of images uses the same measurement rate to measure each block, while different image blocks contain different structural features, the number of measurements needs varies as well. In this paper, a V-AMRS (Adaptive Measurement Rate Setting Method Based on Variance of Classified Image Blocks) method is proposed. According to the variances, the image blocks...
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