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This paper presents a decision support system for classification of hotel guests in the terms of additional spending. The research is conducted on three stars medium-sized hotel. Guests are classified on arrival, during check-in, in one of the two groups: low spending group or high spending group. A low spending group consists of visitors that are anticipated to spend less than 25 Euros per day for...
In this paper, we propose a Wavelet Neural Network (WNN) classifier for breast cancer. WNN is a new kind of artificial neural network which is coming more popular these days. This method is based on the Wavelet Transform (WT) and classical neural networks. This paper explains how WNN classifies and uses formulas. The results of the experiments made to obtain the best results and the parameters affecting...
To improve the accuracy of surface defect detection, an approach of defect inspection based on visual saliency map and Support Vector Machine(SVM) is proposed. Monochrome fabric defect images are taken as examples in this paper. By analyzing the visual saliency maps of these images, the global associated value and the background associated value are extracted as the two features. After being normalized,...
Extreme Learning Machine (ELM) is a neural network architecture with Single Layer Feed-forward Neural Network (SLFN). For meaningful results, the structure of ELM has to be optimized through the inclusion of regularization and the ℓ2 — norm based regularization is mostly used. ℓ2-norm based regularization achieves better performance than the traditional ELM. The estimate of the regularization parameter...
This paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under...
The success of machine learning (ML) algorithms depends on the quality of data given to them. If the input data contains insufficient or irrelevant features, the accuracy of machine learning algorithm decreases. Attribute selection has a key role in creation of classification models. Based on the ‘logic behind the inference’ principle in the Nyaya school of thought, this paper proposes a new method...
In this paper, an extraction and classification of steady state-visual evoked potentials using the IIR Chebyshev I of 4 order and the adaptive feed-forward Neural Networks algorithm, respectively are applied. The classification results of the extracted signals is directly used to make a user able of controlling the directions (stop, forward, right, and left with stimuli frequencies of 7.5, 10, 15,...
Recognizing secondary structures in proteins can be a highly computationally expensive task that may not always yield good results. Using Restricted Boltzmann Machines (RBM) we were able to train a simple neural network to recognize an alpha-helix with a good degree of accuracy. Modifying the RBM implementation to be much simpler and more efficient than the standard implementation we are able to see...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrixand generates two dimensional coordinates. By measuring thedistance between categories and the assigned points, ranking of key wordswill...
Alzheimer's disease (AD) cannot be cured or slowed down with today's medication. Scientific studies have found that 1) the progression of AD is highly correlated to a cognition decline, 2) cognition drop is a precursor of Alzheimer's disease, and 3) making lifestyle changes and training the brain can slow down AD progression. This project aims to develop a predictive model to know the progression...
Neuroscience researchers have a keen interest in finding the connection between various brain regions of an organism. Researchers all across the globe are finding new connections everyday and it is very difficult to keep track of all those, so it is important to create a centralized system which is able to give the relation between brain entities. Databases like PubMed contains abstracts and references...
Color is one of the attributes that play a role in identifying specific objects, color processing including the extraction of information about the spectral properties of the object's surface and look for the best similarity of a set of descriptions which have been known to do an introduction. Therefore, the classification is needed right fuji apples to obtain good quality fruit. Fuzzy model is one...
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 this paper, we proposed an optimum combination of sub-band power features method for improving the classification accuracy rate of left- or right-hand movement imagery electroencephalogram signals. The sub-band power features were extracted from the best time segment of electroencephalogram trials and the proposed training model determined the optimum combination of sub-bands. Our approach was...
We propose a recursive singular value decomposition (SVD)-based fuzzy extreme learning machine (RSVD-F-ELM) for the online learning in classification or regression analysis. By adopting the same architecture and operation as fuzzy extreme learning machine (F-ELM), which is originally designed for the batch learning, and replacing the Moore-Penrose generalized inverse in F-ELM with a recursive SVD-based...
This paper aims to identify lead users from an online user innovation community. Based on three dimensions of user characteristics — user activeness, community influence, and user relations, a Random Forest classification model for lead user identification is proposed. Using the data from the MIUI forum of Xiaomi community, this model is tested. The result shows that Random Forest classification based...
Sparse representation (SR) based hyperspectral image (HSI) classification is a rapidly evolving research topic. How to construct an optimized dictionary to better characterize spectral-spatial features of HSI is an important problem. In this paper, a novel spectral-spatial online dictionary learning (SSODL) method for HSI classification is proposed. The main idea is to learn a complete and discriminative...
In the hyperspectral remote sensing community, decision forests combine the predictions of multiple decision trees (DTs) to achieve better prediction performance. Two well-known and powerful decision forests are Random Forest (RF) and Rotation Forest (RoF). In this work, a novel decision forest, called Partial Least Square Forest (PLSF), is proposed. In the PLSF, we adapt PLS to obtain the components...
Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source of information. Speckle noise gives SAR images a granular appearance that makes interpretation and analysis hard tasks. A major issue is the assessment of information content in these kind of images, and how it is affected by usual processing techniques. We study this problem from the classification accuracy viewpoint. Our...
In this work, a diversified deep structural metric learning is proposed for remote sensing image classification. Firstly, a deep structural metric learning is introduced to take full advantage of structural information of training batches. Secondly, we impose a diversity regularization over the factors of deep structural metric learning to encourage them to be uncorrelated, such that each factor tends...
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