Kernel methods in system identification, machine learning and function estimation: A survey

نویسندگان

  • Gianluigi Pillonetto
  • Francesco Dinuzzo
  • Tianshi Chen
  • Giuseppe De Nicolao
  • Lennart Ljung
چکیده

Most of the currently used techniques for linear system identification are based on classical estimation paradigms coming from mathematical statistics. In particular, maximum likelihood and prediction error methods represent the mainstream approaches to identification of linear dynamic systems, with a long history of theoretical and algorithmic contributions. Parallel to this, in the machine learning community alternative techniques have been developed. Until recently, there has been little contact between these two worlds. The first aim of this survey is to make accessible to the control community the key mathematical tools and concepts as well as the computational aspects underpinning these learning techniques. In particular, we focus on kernel-based regularization and its connections with reproducing kernel Hilbert spaces and Bayesian estimation of Gaussian processes. The second aim is to demonstrate that learning techniques tailored to the specific features of dynamic systemsmayoutperform conventional parametric approaches for identification of stable linear systems. © 2014 Elsevier Ltd. All rights reserved.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

یادگیری نیمه نظارتی کرنل مرکب با استفاده از تکنیک‌های یادگیری معیار فاصله

Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown t...

متن کامل

تشخیص سرطان پستان با استفاده از برآورد ناپارمتری چگالی احتمال مبتنی بر روش‌‌های هسته‌ای

Introduction: Breast cancer is the most common cancer in women. An accurate and reliable system for early diagnosis of benign or malignant tumors seems necessary. We can design new methods using the results of FNA and data mining and machine learning techniques for early diagnosis of breast cancer which able to detection of breast cancer with high accuracy. Materials and Methods: In this study,...

متن کامل

Gene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method

Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...

متن کامل

Investigating the performance of machine learning-based methods in classroom reverberation time estimation using neural networks (Research Article)

Classrooms, as one of the most important educational environments, play a major role in the learning and academic progress of students. reverberation time, as one of the most important acoustic parameters inside rooms, has a significant effect on sound quality. The inefficiency of classical formulas such as Sabin, caused this article to examine the use of machine learning methods as an alternat...

متن کامل

MODELING OF FLOW NUMBER OF ASPHALT MIXTURES USING A MULTI–KERNEL BASED SUPPORT VECTOR MACHINE APPROACH

Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel funct...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Automatica

دوره 50  شماره 

صفحات  -

تاریخ انتشار 2014