다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

A novel method for blind separation of digital signals based on elitist genetic algorithms is presented in this paper. Contrast function, consisting in a weighted sum of high order statistics measures (cumulants of different orders), plays the role of genetic fitness function, and also guide the genetic algorithm by a Gauss-Newton adaptation applied to the genetic population, that reduces the search space and provide faster convergence rate. The use of elitism assures the convergence of the algorithm. Several experiments were conducted on digital signals and mixing models, and the high amount of simulations derived from them provided the best combination of the constant parameters in terms of separation accuracy and convergence rate. In this sense, we also achieve a robust blind source separation method that efficiently adapts to the statistical nature of the mixing signals, within a low population of the genetic algorithm.

Elitist genetic algorithm guided by higher order statistic for blind separation of digital signals

In this contribution, we propose and analyze three evaluation functions (contrast functions in Independent Component Analysis terminology) for the use in a genetic algorithm (PNL-GABSS, Post-NonLinear Genetic Algorithm for Blind Source Separation) which solves source separation in nonlinear mixtures, assuming the post-nonlinear mixture model. Blind source separation refers to the problem of recovering a set of unknown sources from another set of mixtures directly observable and little more information about the way they were mixed. Assuming statistical independence as the assumption to obtain the original sources we can apply ICA (Independent Component Analysis) as the technique to recover the signals. In order to analyze in practice the performance of the chosen fitness functions in our proposed algorithm, we applied ANOVA (Analysis of Variance) to the results, showing the validity of the three approaches.

Assessing the performance of several fitness functions in a genetic algorithm for nonlinear separation of sources

MVPAlab is a MATLAB-based and very flexible decoding toolbox for multidimensional electroencephalography and mag-netoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivari-ate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution anal-yses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrials generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. This toolbox has been designed to include an easy-to-use and very intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for those users with few or no previous coding experience. However, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.

/pdf/mvpalab-a-machine-learning-decoding-toolbox-for-4brhhi7qmg.pdf

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

This paper shows a fuzzy logic speech/non-speech discrimination method for improving the performance of speech processing systems working in noise environments. The fuzzy system is based on a Sugeno inference engine with membership functions defined as combination of two Gaussian functions. The rule base consists of ten fuzzy if then statements defined in terms of the denoised subband signal-to-noise ratios (SNRs) and the zero crossing rates (ZCRs). Its operation is optimized by means of a hybrid training algorithm combining the least-squares method and the backpropagation gradient descent method for training membership function parameters. The experiments conducted on the Spanish SpeechDat-Car database shows that the proposed method yields clear improvements over a set of standardized VADs for discontinuous transmission (DTX) and distributed speech recognition (DSR) and also over recently published VAD methods.

/pdf/fuzzy-logic-speech-non-speech-discrimination-for-noise-233kqih83v.pdf

Fuzzy logic speech/non-speech discrimination for noise robust speech processing

An effective speech event detector is presented in this work for improving the performance of speech processing systems working in noisy environment. The proposed method is based on a trained support vector machine (SVM) that defines an optimized non-linear decision rule involving the subband SNRs of the input speech. It is analyzed the classification rule in the input space and the ability of the SVM model to learn how the signal is masked by the background noise. The algorithm also incorporates a noise reduction block working in tandem with the voice activity detector (VAD) that has shown to be very effective in high noise environments. The experimental analysis carried out on the Spanish SpeechDat-Car database shows clear improvements over standard VADs including ITU G.729, ETSI AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs.

/pdf/speech-event-detection-using-support-vector-machines-2el8y9edyj.pdf

Juan Manuel Górriz

Papers

Elitist genetic algorithm guided by higher order statistic for blind separation of digital signals

Assessing the performance of several fitness functions in a genetic algorithm for nonlinear separation of sources

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Fuzzy logic speech/non-speech discrimination for noise robust speech processing

Speech event detection using support vector machines