{"id":554,"date":"2020-12-19T23:40:56","date_gmt":"2020-12-19T22:40:56","guid":{"rendered":"http:\/\/honeine.fr\/wp\/?page_id=554"},"modified":"2020-12-19T23:46:25","modified_gmt":"2020-12-19T22:46:25","slug":"time-frequency-learning-machines","status":"publish","type":"page","link":"https:\/\/honeine.fr\/wp\/research\/time-frequency-learning-machines\/","title":{"rendered":"Time-Frequency Learning Machines"},"content":{"rendered":"\n<p class=\"has-small-font-size\">The theory of reproducing kernels has made a major breakthrough in the field of pattern recognition. It has led to new algorithms, with improved performance and lower computational cost, for non-linear analysis in high dimensional feature spaces.<\/p>\n\n\n\n<p class=\"has-small-font-size\">These works extend the framework of the kernel learning machines to time-frequency analysis, showing that some specific reproducing kernels allow these algorithms to operate in the time-frequency domain. This link offers new perspectives in the field of non-stationary signal analysis, which can benefit from the developments of pattern recognition and Statistical Learning Theory.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"729\" height=\"157\" src=\"https:\/\/honeine.fr\/wp\/wp-content\/uploads\/droppedImage-filtered.jpg\" alt=\"\" class=\"wp-image-555\" srcset=\"https:\/\/honeine.fr\/wp\/wp-content\/uploads\/droppedImage-filtered.jpg 729w, https:\/\/honeine.fr\/wp\/wp-content\/uploads\/droppedImage-filtered-300x65.jpg 300w\" sizes=\"auto, (max-width: 729px) 100vw, 729px\" \/><figcaption>Figure: The most relevant eigen-distributions in the time-frequency domain of K-complex signals in EEG recordings.<\/figcaption><\/figure><\/div>\n\n\n\n<h1 class=\"wp-block-heading\"><span class=\"has-inline-color has-vivid-cyan-blue-color\">Selected Publications<\/span><\/h1>\n\n\n\n<p class=\"has-small-font-size\">P. HONEINE, C. RICHARD, and P. FLANDRIN<br><strong>Nonstationary signal analysis with kernel machines<\/strong><br><em>chapter in\u00a0Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, <\/em>Publisher: Information Science Reference, IGI Global, ISBN: 1605667668,\u00a0pp 223-241 2010.<\/p>\n\n\n\n<p class=\"has-small-font-size\">P. HONEINE, C. RICHARD, and P. FLANDRIN<br><strong>Time-frequency learning machines<\/strong><br><em>IEEE Trans. Signal Processing<\/em>, 55:3930-3936, 2007.<\/p>\n\n\n\n<p class=\"has-small-font-size\">P. HONEINE, and C. RICHARD<br><strong>Distribution temps-fr\u00e9quence \u00e0 param\u00e9trisation radialement Gaussienne optimis\u00e9e pour la classification<\/strong><br>English: Optimizing time-frequency representations for signal classification using radially Gaussian kernels<br><em>Traitement du signal<\/em>, 25(6), 2008. (invited french paper)<\/p>\n\n\n\n<p class=\"has-small-font-size\">P. HONEINE, and C. RICHARD<br><strong>Signal-dependent time-frequency representations for classification using a radially gaussian kernel and the alignment criterion<\/strong><br><em>Proc. of\u00a0IEEE Statistical Signal Processing Workshop (SSP)<\/em>, Madison (WI), USA, 26-29 Aug. 2007.<br>[paper] [poster] [bibTeX]<\/p>\n\n\n\n<p class=\"has-small-font-size\">P. HONEINE, C. RICHARD, P. FLANDRIN, and J.-B. POTHIN<br><strong>Optimal selection of time-frequency representations for signal classification: a kernel-target alignment approach<\/strong><br><em>Proc. of\u00a0IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)<\/em>, Toulouse, France, 14-19 May 2006.<br>[paper] [poster] [bibTeX]<\/p>\n\n\n\n<p class=\"has-small-font-size\">P. HONEINE, et C. RICHARD<br><strong>Distribution temps-fr\u00e9quence \u00e0 noyau radialement gaussien\u00a0: optimisation pour la classification par le crit\u00e8re d&#8217;alignement noyau-cible<\/strong><br><em>Actes du\u00a0XXI-\u00e8me colloque GRETSI\u00a0: Traitement du signal et des images<\/em>, Troyes, France, 2007.<br>[paper] [bibTeX]<\/p>\n\n\n\n<p class=\"has-small-font-size\">P. HONEINE, C. RICHARD, et P. FLANDRIN<br><strong>Reconnaissance des formes par m\u00e9thodes \u00e0 noyau dans le domaine temps-fr\u00e9quence<\/strong><br><em>Actes du\u00a0XX-\u00e8me colloque GRETSI\u00a0: Traitement du signal et des images<\/em>, Louvain-la-neuve, Belgium, 2005.<br>[paper] [bibTeX]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The theory of reproducing kernels has made a major breakthrough in the field of pattern recognition. It has led to new algorithms, with improved performance and lower computational cost, for non-linear analysis in high dimensional feature spaces. These works extend the framework of the kernel learning machines to time-frequency analysis, showing that some specific reproducing [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":493,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-554","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/pages\/554","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/comments?post=554"}],"version-history":[{"count":3,"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/pages\/554\/revisions"}],"predecessor-version":[{"id":559,"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/pages\/554\/revisions\/559"}],"up":[{"embeddable":true,"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/pages\/493"}],"wp:attachment":[{"href":"https:\/\/honeine.fr\/wp\/wp-json\/wp\/v2\/media?parent=554"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}