See also my Google Scholar page. In this paper, we propose a novel approach for weakly-supervised word recognition. Most state of the art automatic speech recognition systems are based on frame-level labels obtained through forced alignments or through a sequential loss.
Show Context Citation Context For classification, a class decision is based upon whether the v Gaussian mixture models with universal backgrounds UBMs have become the standard method for speaker recognition.
A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation.
We consider this GMM supervector in the context of support vector machines.
We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel between the method of SVM nuisance attribute projection NAP and the recent results in latent factor analysis. For classification, a class decision is based upon whether the va Our Kernel-RLS KRLS algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared -error regressor.
Sparsity of the solution is achieved by a sequential sparsification process that admits into the kernel representation a new input sample only if its feature space image cannot be suffciently well approximated by combining the images of previously admitted samples.
This sparsification procedure is crucial to the operation of KRLS, as it allows it to operate on-line, and by effectively regularizing its solutions. A theoretical analysis of the sparsification method reveals its close affinity to kernel PCA, and a data-dependent loss bound is presented, quantifying the generalization performance of the KRLS algorithm.
We demonstrate the performance and scaling properties of KRLS and compare it to a stateof -the-art Support Vector Regression algorithm, using both synthetic and real data.
We additionally test KRLS on two signal processing problems in which the use of traditional least-squares methods is commonplace: Time series prediction and channel equalization.
Finally, SVM solutions are known to The decomposition method is currently one of the major methods for solving support vector machines.
An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for bound-constrained SVM formulations we demonstrate that the w In this paper through the design of decomposition methods for bound-constrained SVM formulations we demonstrate that the working set selection is not a trivial task.
Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases.
Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method. In addition, as more available software follow the implementation of SV M light using 3 e. In this paper we will show that Algorithm I.
Next we discuss some possi Many scientific communities have expressed a growing interest in machine learning algorithms recently, mainly due to the generally good results they provide, compared to traditional statistical or AI approaches. However, these machine learning algorithms are often complex to implement and to use pro However, these machine learning algorithms are often complex to implement and to use properly and efficiently.
We thus present in this paper a new machine learning software library in which most state-of-the-art algorithms have already been implemented and are available in a unified framework, in order for scientists to be able to use them, compare them, and even extend them.
More interestingly, this library is freely available under a BSD license and can be retrieved on the web by everyone. Support Vector Machines SVMs are currently the state-of-the-art models for many classification problems but they suffer from the complexity of their training algorithm which is at least quadratic with respect to the number of examples.A Machine Learning Approach to Classiﬂcation of Low Resolution Histological Samples MASTER THESIS IN COMPUTER AND COMMUNICATION SCIENCES Author: Gr¶egoire Montavon [email protected]°.ch Malon and Ronan Collobert gave me frequent feedback and valuable advice on my ex-.
This thesis aims to address machine learning in general, with a particu-lar focus on large models and large databases.
After introducing the learning problem in a formal way, we ﬁrst review several important machine learning algorithms, particularly Multi Layer Perceptrons, Mixture of .
For e.g, as a part of my master thesis, I am working on predicting sleep stages using the heart signal. The heart signal varies with the time of our sleep. In the early duration of sleep, when we are still awake, the heart beat is higher and more active.
yann lecun and ronan collobert. Are network guys through and through, as is Geoff Hinton.
This means they use networks for everything. As an example, a couple of months ago, I saw Geoff give a talk on 3d reconstruction/novel view synthesis using neural networks.
by Ronan Collobert, Samy Bengio, Johnny Marithoz, Many scientific communities have expressed a growing interest in machine learning algorithms recently, mainly due to the generally good results they provide, compared to traditional statistical or AI approaches.
Semi-supervised Learning via Generalized Maximum Entropy by Ay˘se Naz Erkan a PhD thesis in two di erent institutions, on two continents. I would like to also thank Rosemary Amico, who has provided enormous help, often beyond her re- Dr. Jason Weston and Dr.
Ronan Collobert during my internship at NEC Research labs.