There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

The measurement of meaning

The PASCAL Visual Object Classes Challenge

3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for 2D images were shown sensitive to lighting condition changes. In this paper, we propose a learning-based approach for reliable locating of face landmarks in 3D. Our approach relies on a statistical model, called 3D Statistical Facial feAture Model(SFAM) in the paper, which learns both global variations in 3D face morphology and local ones around the 3D face landmarks in terms of local texture and shape. Experimented on FRGC v1.0 dataset, our approach shows its effectiveness and achieves 99.09% of locating accuracy in 10mm precision. The mean error and standard deviation of each landmark are respectively less than 5mm and 4.

A 3D Statistical Facial Feature Model and Its Application on Locating Facial Landmarks

The purpose of this paper is to make an automatic classification of speech into seven emotional classes as anger, boredom, disgust, fear, gladness, neutral and sadness A two-stage classification composed of several sub-classifiers is proposed A feature set with 68 features has been computed over 286 speech samples from the Berlin database The Sequential Forward Selection method (SFS) has been used for each classifiers of the two stages in order to decide the feature subsets in each step The result for the first stage as three-state classification is 87%, and the global result of the seven emotional classes is 78%, where the correct recognition rate of random classification by chance is about 15%

Two-stage Classification of Emotional Speech

The classification of affective semantics in images is a very challenging research direction that gains more and more attention in the research community. However, as an emerging topic, contributions remain relatively rare, and a lot of issues need to be addressed particularly concerning the three following fundamentals problems: emotion representation, image features used to represent emotions and classification schemes designed to handle the distinctive characteristics of emotions. Thus, we present in this paper two classification approaches based on the dimensional and discrete emotion models. Traditional and emotional image features are used as input of classifiers relying on neural networks and on the evidence theory whose interesting properties allow to handle the ambiguous and subjective nature of emotions as it has been brought to the fore by our experimental results.

Classification of affective semantics in images based on discrete and dimensional models of emotions

This paper deals with several of the possible uses of Zipf and inverse Zipf laws in the field of audio signal analysis. We show that these laws are powerful analysis tools allowing the extraction of information not available by standard methods. The adaptation of Zipf and inverse Zipf laws to audio signals requires a coding of these signals into text-like data, considered as sequences of patterns. Because these codings are of first importance since they have to bring to the fore relevant information within signals, three types of codings have been developed, depending on the representation of the audio signal it is based on: temporal, frequential and time-scale representations. Once audio signal has been coded, features linked to Zipf and inverse Zipf approaches are computed. Finally, the classification step aims at the identification of signals. Four classification methods have been considered as well as a fusion method that combines these classifiers. In order to evaluate our method, we apply it on medical acoustical signals. They occur when swallowing and contain xiphoidal sounds. The problem is to extract and characterize xiphoidal sounds according to the gastro-oesophageal reflux pathological state. The aim is to help medical doctors to characterize and diagnose this pathology, and to give, in the end, a decision help tool as efficient as possible.

Zipf analysis of audio signals

Feature subset selection is an important subject when training classifiers in Machine Learning (ML) problems. Too many input features in a ML problem may lead to the so-called "curse of dimensionality", which describes the fact that the complexity of the classifier parameters adjustment during training increases exponentially with the number of features. Thus, ML algorithms are known to suffer from important decrease of the prediction accuracy when faced with many features that are not necessary. In this paper, we introduce a novel embedded feature selection method, called ESFS, which is inspired from the wrapper method SFS since it relies on the simple principle to add incrementally most relevant features. Its originality concerns the use of mass functions from the evidence theory that allows to merge elegantly the information carried by features, in an embedded way, and so leading to a lower computational cost than original SFS. This approach has successfully been applied to the emergent domain of emotion classification in audio signals.

https://hal.archives-ouvertes.fr/hal-01984705/document

Emmanuel Dellandréa

Papers

A 3D Statistical Facial Feature Model and Its Application on Locating Facial Landmarks

Two-stage Classification of Emotional Speech

Classification of affective semantics in images based on discrete and dimensional models of emotions

Zipf analysis of audio signals

ESFS: A new embedded feature selection method based on SFS