Robust kernel ridge regression based on M-estimation

Wibowo, Antoni

doi:10.1007/s10598-009-9049-7

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Kewirausahaan bersumber dari istilah entrepeneurship yang berarti kesanggupan (capacity) untuk berpikir kreatif serta bertindak inovatif untuk dijadikan sebagai landasan, sumber daya, motivasi, dalam mencapai tujuan, kiat serta tata cara menghadapi kehidupan beserta tantangan di dalamnya (Wibowo, 2011). Selain itu kewirausahaan memiliki peran multiplier atau efek ganda yang cukup efektif sebagai alternatif dalam mengatasi berbagai permasalahan sosial yang ada, mulai dari pengangguran, kemiskinan, keterbelakangan sosial lainnya.…”

Section: Hasil Penelitian Dan Pembahasanunclassified

Kiat-kiat Mendirikan Usaha Sampingan sebagai Alternatif Seorang Guru dalam Mencapai Kesejahteraan Hidup

Machmud

Subiyantoro

2022

EKL

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Guru memiliki tatanan penting dalam pendidikan, apapun jenis statusnya. Perannya yang berpengaruh besar terhadap hasil pendidikan seharusnya membuat guru mendapatkan balasan yang layak atau setimpal. Kendati demikian, masih banyak dijumpai guru yang tidak sejahtera terlebih segi finansial. Salah satu upaya yang dapat dilakukan guru untuk menyejahterakan finansial yakni dengan memulai membuka usaha. Penelitian ini merupakan penelitian kualitatif deskriptif, menggunakan metode wawancara dalam memperoleh dan menggali data. Adapun jenis wawancara yakni wawancara bebas terpimpin yaitu terjadi komunikasi antara responden dan pewawancara melalui instrumen pertanyaan yang telah dibuat sebelumnya dengan responden sesuai kriteria. Adapun hasil penelitian ini upaya yang dapat dilakukan dalam mendirikan usaha meliputi 1). Memiliki niat/kemauan yang tinggi, 2). Memilih bidang usaha yang diminati sembari membaca peluang pasar, 3). Menentukan modal, 4). Action, 5). Menciptakan dan menjalin kepercayaan konsumen 6). Menambah relasi, 7). Memiliki keunikan/image branding, 8). Menjaga kredibilitas, 9). Mengatur biaya operasional, 10). Memercayai sisi spiritual.

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Section: Hasil Penelitian Dan Pembahasanunclassified

Kiat-kiat Mendirikan Usaha Sampingan sebagai Alternatif Seorang Guru dalam Mencapai Kesejahteraan Hidup

Machmud

Subiyantoro

2022

EKL

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“…Previous work combining robust estimation and kernel methods has focused primarily on supervised learning problems. M -estimation applied to kernel regression has been studied by various authors (Christmann & Steinwart, 2007;Debruyne et al, 2008a,b;Zhu et al, 2008;Wibowo, 2009;Brabanter et al, 2009). Robust surrogate losses for kernel-based classifiers have also been studied (Xu et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Robust Kernel Density Estimation

Kim¹,

Scott²

2011

Preprint

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We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density estimator (KDE) with ideas from classical M -estimation.We interpret the KDE based on a radial, positive semi-definite kernel as a sample mean in the associated reproducing kernel Hilbert space. Since the sample mean is sensitive to outliers, we estimate it robustly via M -estimation, yielding a robust kernel density estimator (RKDE).An RKDE can be computed efficiently via a kernelized iteratively re-weighted least squares (IRWLS) algorithm. Necessary and sufficient conditions are given for kernelized IRWLS to converge to the global minimizer of the M -estimator objective function. The robustness of the RKDE is demonstrated with a representer theorem, the influence function, and experimental results for density estimation and anomaly detection.

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Data‐driven approach for fault detection and isolation in nonlinear system

Kallas

Mourot

Maquin

et al. 2018

Adaptive Control & Signal

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SummarySystem diagnosis has been of a great interest for all aspects of industrial processes more precisely to gain in quality. It is based essentially on the analysis of the links between the variables of a system and more precisely on the changes of the relations between these variables, which testify to the presence of faults or anomalies. For that purpose, data modeling is the process of finding a mathematical expression that provides a good fit between given finite sample values of the independent variables and the associated values of the dependent variables of the process.The aim of this paper is to detect and, above all, localize faults affecting a system with nonlinear behavior, when its model is not known a priori. An important part of the presentation is dedicated to the construction of fault indicators capable of locating faults, ie, recognizing the input or output of a system affected by a fault. The first part of this paper is devoted to how to predict the output of a nonlinear behavior system. The second part proposes a way for the detection and isolation of measurement faults based on the proposed prediction model. The relevance of the proposed technique, for modeling and system diagnosis, is illustrated on a simulated example in the context of SIMO and MIMO systems.

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Robust kernel ridge regression based on M-estimation

Cited by 4 publications

References 9 publications

Kiat-kiat Mendirikan Usaha Sampingan sebagai Alternatif Seorang Guru dalam Mencapai Kesejahteraan Hidup

Kiat-kiat Mendirikan Usaha Sampingan sebagai Alternatif Seorang Guru dalam Mencapai Kesejahteraan Hidup

Robust Kernel Density Estimation

Data‐driven approach for fault detection and isolation in nonlinear system

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