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Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass Classification This page describes the accompanying software for the following paper: Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass Classification. [PDF, BibTeX] Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara. Machine Learning, May 2013. The paper will also be presented at ECML/PKDD 2013. If you use
I’m thrilled to announce that my paper “Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass Classification” (published in the Machine Learning journal) is now online: PDF, BibTeX [*]. Abstract Over the past decade, l1 regularization has emerged as a powerful way to learn classifiers with implicit feature selection. More recently, mixed-norm (e.g., l1/l2) regularization has been ut
Machine Learning, Data Mining, Natural Language Processing… Recently, I’ve been working on a new handwriting recognition engine for Tegaki based on Dynamic Time Warping and I figured it would be interesting to make a short, informal introduction to it. Dynamic Time Warping (DTW) is a well-known algorithm which aims at comparing and aligning two sequences of data points (a.k.a time series). Althoug
scikit-learn Mathieu Blondel 2012 6 13 Mathieu Blondel Machine Learning in Python with scikit-learn 3 scikit-learn Mathieu Blondel Machine Learning in Python with scikit-learn scikit-learn Python 2007 2010 INRIA1 1 Mathieu Blondel Machine Learning in Python with scikit-learn scikit-learn API Mathieu Blondel Machine Learning in Python with scikit-learn scikit-learn v0.11 10 40 github watchers 600
Machine Learning, Data Mining, Natural Language Processing… Codename Project Tegaki I wrote in a previous post about my first experiment with applying a modern technique, namely Hidden Markov Models, for handwritten Chinese character recognition. I’m quite motivated in making this more than just a single isolated experiment so I decided to give a name to the project. I named it Project Tegaki. Thi
Like Latent Semantic Analysis (LSA) and probabilistic LSA (pLSA) – see my previous post “LSA and pLSA in Python“, Latent Dirichlet Allocation (LDA) is an algorithm which, given a collection of documents and nothing more (no supervision needed), can uncover the “topics” expressed by documents in that collection. LDA can be seen as a Bayesian extension of pLSA. As Blei, the author of LDA, points ou
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