993 research outputs found

    Support Vector Machines in R

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    Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.

    Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

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    This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version

    Evolutionary Granular Kernel Machines

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    Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research. For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. Granular Kernel Tree Structure Evolving System (GKTSES) is developed to evolve the structures of Granular Kernel Trees (GKTs) without prior knowledge. A voting scheme is also proposed to reduce the prediction deviation of GKTSES. To speed up EGKTs optimization, a master-slave parallel model is implemented. To help SVMs challenge large-scale data mining, a Minimum Enclosing Ball (MEB) based data reduction method is presented, and a new MEB-SVM algorithm is designed. All these kernel methods are designed based on Granular Computing (GrC). In general, Evolutionary Granular Kernel Machines (EGKMs) are investigated to optimize kernels effectively, speed up training greatly and mine huge amounts of data efficiently

    Variable illumination and invariant features for detecting and classifying varnish defects

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    This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn\u27t provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction

    Multi Criteria Mapping Based on SVM and Clustering Methods

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    There are many more ways to automate the application process like using some commercial software’s that are used in big organizations to scan bills and forms, but this application is only for the static frames or formats. In our application, we are trying to automate the non-static frames as the study certificate we get are from different counties with different universities. Each and every university have there one format of certificates, so we try developing a very new application that can commonly work for all the frames or formats. As we observe many applicants are from same university which have a common format of the certificate, if we implement this type of tools, then we can analyze this sort of certificates in a simple way within very less time. To make this process more accurate we try implementing SVM and Clustering methods. With these methods we can accurately map courses in certificates to ASE study path if not to exclude list. A grade calculation is done for courses which are mapped to an ASE list by separating the data for both labs and courses in it. At the end, we try to award some points, which includes points from ASE related courses, work experience, specialization certificates and German language skills. Finally, these points are provided to the chair to select the applicant for master course ASE

    Advanced Strategies for Robot Manipulators

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    Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

    Pattern Recognition

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    Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

    Worldwide Weather Forecasting by Deep Learning

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    La prĂ©vision mĂ©tĂ©orologique a Ă©tĂ© et demeure une tĂąche ardue ayant Ă©tĂ© approchĂ©e sous plusieurs angles au fil des annĂ©es. Puisque les modĂšles proĂ©minents rĂ©cents sont souvent des modĂšles d’appentissage machine, l’importance de la disponibilitĂ©, de la quantitĂ© et de la qualitĂ© des donnĂ©es mĂ©tĂ©orologiques augmente. De plus, la revue des proĂ©minents modĂšles d’apprentissage profond appliquĂ©s Ă  la prĂ©diction de sĂ©ries chronologiques mĂ©tĂ©orologiques suggĂšre que leur principale limite est la formulation et la structure des donnĂ©es qui leur sont fournies en entrĂ©e, ce qui restreint la portĂ©e et la complexitĂ© des problĂšmes qu’ils tentent de rĂ©soudre. À cet effet, cette recherche fournit une solution, l’algorithme d’interpolation gĂ©ospatiale SkNNI (interpolation des k plus proches voisins sphĂ©rique), pour transformer et structurer les donnĂ©es gĂ©ospatiales disparates de maniĂšre Ă  les rendre utiles pour entraĂźner des modĂšles prĂ©dictifs. SkNNI se dĂ©marque des algorithmes d’interpolation gĂ©ospatiale communs, principalement de par sa forte robustesse aux donnĂ©es d’observation bruitĂ©es ainsi que sa considĂ©ration accrue des voisinages d’interpolation. De surcroĂźt, Ă  travers la conception, l’entraĂźnement et l’évaluation de l’architecture de rĂ©seau de neurones profond DeltaNet, cette recherche dĂ©montre la faisabilitĂ© et le potentiel de la prĂ©diction mĂ©tĂ©orologique multidimensionnelle mondiale par apprentissage profond. Cette approche fait usage de SkNNI pour prĂ©traiter les donnĂ©es mĂ©tĂ©orologiques en les transformant en cartes gĂ©ospatiales Ă  multiples canaux mĂ©tĂ©orologiques qui sont organisĂ©es et utilisĂ©es en tant qu’élĂ©ments de sĂ©ries chronologiques. Ce faisant, le recours Ă  de telles cartes gĂ©ospatiales ouvre de nouveaux horizons quant Ă  la dĂ©finition et Ă  la rĂ©solution de problĂšmes de prĂ©visions gĂ©ospatiales (p. ex. mĂ©tĂ©orologiques) plus complexes. ----------ABSTRACT: Weather forecasting has been and still is a challenging task which has been approached from many angles throughout the years. Since recent state-of-the-art models are often machine learning ones, the importance of weather data availability, quantity and quality rises. Also, the review of prominent deep learning models for weather time series forecasting suggests their main limitation is the formulation and structure of their input data, which restrains the scope and complexity of the problems they attempt to solve. As such, this work provides a solution, the spherical k-nearest neighbors interpolation (SkNNI) algorithm, to transform and structure scattered geospatial data in a way that makes it useful for predictive model training. SkNNI shines when compared to other common geospatial interpolation methods, mainly because of its high robustness to noisy observation data and acute interpolation neighborhood awareness. Furthermore, through the design, training and evaluation of the DeltaNet deep neural network architecture, this work demonstrates the feasibility and potential of multidimensional worldwide weather forecasting by deep learning. This approach leverages SkNNI to preprocess weather data into multi-channel geospatial weather frames, which are then organized and used as time series elements. Thus, working with such geospatial frames opens new avenues to define and solve more complex geospatial (e.g. weather) forecasting problems
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