Skip to main content Accesibility Help
×
×
Home
Kernel Methods for Pattern Analysis
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 1964
  • Cited by
    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Paul, Anal and Maity, Santi P. 2019. Advances in Intelligent Computing. Vol. 687, Issue. , p. 77.

    Cipollini, Francesca Oneto, Luca Coraddu, Andrea and Savio, Stefano 2019. Unsupervised Deep Learning for Induction Motor Bearings Monitoring. Data-Enabled Discovery and Applications, Vol. 3, Issue. 1,

    Pelamatti, Julien Brevault, Loïc Balesdent, Mathieu Talbi, El-Ghazali and Guerin, Yannick 2019. Surrogate model based optimization of constrained mixed variable problems: application to the design of a launch vehicle thrust frame.

    Stapor, Katarzyna Roterman-Konieczna, Irena and Fabian, Piotr 2019. Machine Learning Paradigms. Vol. 149 , Issue. , p. 101.

    Branitskiy, Alexander and Kotenko, Igor 2019. AI in Cybersecurity. Vol. 151, Issue. , p. 115.

    Markovsky, Ivan 2019. Low-Rank Approximation. p. 1.

    Zhang, Lei Zhen, Xiantong Shao, Ling and Song, Jingkuan 2019. Learning Match Kernels on Grassmann Manifolds for Action Recognition. IEEE Transactions on Image Processing, Vol. 28, Issue. 1, p. 205.

    Allappa, Sony S. Thenkanidiyoor, Veena and Dinesh, Dileep Aroor 2019. Pattern Recognition Applications and Methods. Vol. 11351, Issue. , p. 164.

    Das, Asha Nair, Madhu S. and Peter, S. David 2019. Sparse Representation Over Learned Dictionaries on the Riemannian Manifold for Automated Grading of Nuclear Pleomorphism in Breast Cancer. IEEE Transactions on Image Processing, Vol. 28, Issue. 3, p. 1248.

    Bai, Lianfa Han, Jing and Yue, Jiang 2019. Night Vision Processing and Understanding. p. 1.

    Kushwaha, Neetu and Pant, Millie 2018. Fuzzy magnetic optimization clustering algorithm with its application to health care. Journal of Ambient Intelligence and Humanized Computing,

    Bellaouar, Slimane Cherroun, Hadda and Ziadi, Djelloul 2018. Efficient geometric-based computation of the string subsequence kernel. Data Mining and Knowledge Discovery, Vol. 32, Issue. 2, p. 532.

    Domeniconi, Carlotta 2018. Encyclopedia of Database Systems. p. 2072.

    Wang, Qian and Xu, Yitian 2018. Concave-Convex Programming for Ramp Loss-Based Maximum Margin and Minimum Volume Twin Spheres Machine. Neural Processing Letters,

    Escobar-Vega, Luis Miguel Zaldívar-Carrillo, Víctor Hugo and Villalon-Turrubiates, Ivan 2018. Advances in Computational Intelligence. Vol. 11289, Issue. , p. 83.

    Nguyen, Chuong H Karavas, George K and Artemiadis, Panagiotis 2018. Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features. Journal of Neural Engineering, Vol. 15, Issue. 1, p. 016002.

    Cohen, Achraf Messaoudi, Chaimaa and Badir, Hassan 2018. New Frontiers of Biostatistics and Bioinformatics. p. 175.

    Duda, Piotr Jaworski, Maciej and Rutkowski, Leszek 2018. Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks. International Journal of Neural Systems, Vol. 28, Issue. 02, p. 1750048.

    Wang, Tinghua and Liu, Fulai 2018. PRICAI 2018: Trends in Artificial Intelligence. Vol. 11012, Issue. , p. 246.

    Sharif, Uzma Mehmood, Zahid Mahmood, Toqeer Javid, Muhammad Arshad Rehman, Amjad and Saba, Tanzila 2018. Scene analysis and search using local features and support vector machine for effective content-based image retrieval. Artificial Intelligence Review,

    ×

Book description

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

Reviews

'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.'

Source: SIAM Review

'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.'

Source: Computing Reviews

' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.'

Source: IAPR Newsletter

Refine List
Actions for selected content:
Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Send to Kindle
  • Send to Dropbox
  • Send to Google Drive
  • Send content to

    To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to .

    To send content items to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

    Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

    Find out more about the Kindle Personal Document Service.

    Please be advised that item(s) you selected are not available.
    You are about to send
    ×

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed