Search results for "Intelligence"
showing 10 items of 6959 documents
The impact of feature extraction on the performance of a classifier : kNN, Naïve Bayes and C4.5
2005
"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and the classification error in high dimensions. In this paper, different feature extraction techniques as means of (1) dimensionality reduction, and (2) constructive induction are analyzed with respect to the performance of a classifier. Three commonly used classifiers are taken for the analysis: kNN, Naïve Bayes and C4.5 decision tree. One of the main goals of this paper is to show the importance of the use of class information in feature extraction for classification and (in)appropriateness of random projection or conventional PCA to feature extraction for …
Vector anisotropic filter for multispectral image denoising
2015
In this paper, we propose an approach to extend the application of anisotropic Gaussian filtering for multi- spectral image denoising. We study the case of images corrupted with additive Gaussian noise and use sparse matrix transform for noise covariance matrix estimation. Specifically we show that if an image has a low local variability, we can make the assumption that in the noisy image, the local variability originates from the noise variance only. We apply the proposed approach for the denoising of multispectral images corrupted by noise and compare the proposed method with some existing methods. Results demonstrate an improvement in the denoising performance.
Linear transform for simultaneous diagonalization of covariance and perceptual metric matrix in image coding
2003
Two types ofredundancies are contained in images: statistical redundancy and psychovisual redundancy. Image representation techniques for image coding should remove both redundancies in order to obtain good results. In order to establish an appropriate representation, the standard approach to transform coding only considers the statistical redundancy, whereas the psychovisual factors are introduced after the selection ofthe representation as a simple scalar weighting in the transform domain. In this work, we take into account the psychovisual factors in the de8nition of the representation together with the statistical factors, by means of the perceptual metric and the covariance matrix, res…
Sign and Rank Covariance Matrices: Statistical Properties and Application to Principal Components Analysis
2002
In this paper, the estimation of covariance matrices based on multivariate sign and rank vectors is discussed. Equivariance and robustness properties of the sign and rank covariance matrices are described. We show their use for the principal components analysis (PCA) problem. Limiting efficiencies of the estimation procedures for PCA are compared.
Attitude estimation from polarimetric cameras
2018
International audience; In the robotic field, navigation and path planning applications benefit from a wide range of visual systems (e.g. perspective cameras, depth cameras, catadioptric cameras, etc.). In outdoor conditions, these systems capture information in which sky regions cover a major segment of the images acquired. However, sky regions are discarded and are not considered as visual cue in vision applications. In this paper, we propose to estimate attitude of Unmanned Aerial Vehicle (UAV) from sky information using a polarimetric camera. Theoretically , we provide a framework estimating the attitude from the skylight polarized patterns. We showcase this formulation on both simulate…
Improving Nearest Neighbor Based Multi-target Prediction Through Metric Learning
2017
The purpose of this work is to learn specific distance functions to be applied for multi-target regression problems using nearest neighbors. The idea of preserving the order relation between input and output vectors considering their corresponding distances is used along a maximal margin criterion to formulate a specific metric learning problem. Extensive experiments and the corresponding discussion try to put forward the advantages of the proposed algorithm that can be considered as a generalization of previously proposed approaches. Preliminary results suggest that this line of work can lead to very competitive algorithms with convenient properties.
Covid-19 Anxiety Relationship with Emotional Intelligence and Burnout Clinical Subtypes Among Organizational Staff
2022
Maģistra darba “Covid-19 trauksmes saistība ar emocionālo intelektu un klīniskajiem izdegšanas apakštipiem organizāciju darbinieku vidū” mērķis ir noskaidrot vai un kādas saistības pastāv Covid-19 trauksmes, emocionālā intelekta un klīnisko izdegšanas apakštipu starpā. Papildus iepriekš minētajam, darba mērķis ir arī pārbaudīt hipotēzi, ka darbinieki, kuri strādā daļēji vai pilnībā attālināti, izjūt lielāku Covid-19 trauksmi, salīdzinot ar darbiniekiem, kuri ikdienā strādā klātienē. Pētījumā piedalījās 130 organizāciju darbinieki vecumā no 20 līdz 55 gadiem, no kuriem 56 strādā klātienē, 42 – attālināti, bet 32 - hibrīdajā modelī. Izmantotas trīs aptaujas - “Izdegšanas klīnisko apakštipu ap…
Critical path analysis in the network with fuzzy activity times
2001
A natural generalization of the criticality notion in a network with fuzzy activity times is given. It consists in direct application of the extension principle of Zadeh to the notion of criticality of a path (an activity, an event) treated as a function of the activities duration times in the network. There are shown some relations between the notion of fuzzy criticality, introduced in the paper, and the notion of interval criticality (criticality in the network with interval activity times) proposed by the authors in another paper. Two methods of calculation of the path degree of criticality (according to the proposed concept of fuzzy criticality) are presented.
Criticality in the Network with Imprecise Activity Times
2002
A review of the results obtained in the area of fuzzy network analysis is presented. The main approaches to the concept of criticality in a network with fuzzy activity times are described and classified. Against the background of this review some new results, obtained by the authors recently, are presented. The paper is an extended version of the work presented in IPMU'2000 (see [8]).
Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification
2019
Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-…