Coopération modélisation : discrimination pour la reconnaissance d'écriture manuscrite

-Reconnaître l'écriture manuscrite est un problème d'une telle complexité qu'il est devenu courant de faire coopérer plusieurs algorithmes de classification. Dans cet article, nous présentons un classifieur hybride original. Un premier expert de modélisation détermine les deux classes les plus pertinentes en comparant le symbole inconnu à un ensemble exhaustif de symboles. Le second, discriminant, permet de lever les ambiguïtés. Cette architecture hybride exploite le fait que la "bonne" classe appartient le plus souvent aux deux classes les plus pertinentes trouvées par le premier classifieur. Les expérimentations, conduites sur une base de test de 20000 formes (62 classes), montrent que l'apport relatif de la coopération s'élève à 30%

Development of a cytology-based multivariate analytical risk index for oral cancer

Objectives The diagnosis and management of oral cavity cancers are often complicated by the uncertainty of which patients will undergo malignant transformation, obligating close surveillance over time. However, serial biopsies are undesirable, highly invasive, and subject to inherent issues with poor inter-pathologist agreement and unpredictability as a surrogate for malignant transformation and clinical outcomes. The goal of this study was to develop and evaluate a Multivariate Analytical Risk Index for Oral Cancer (MARIO) with potential to provide non-invasive, sensitive, and quantitative risk assessments for monitoring lesion progression. Materials and methods A series of predictive models were developed and validated using previously recorded single-cell data from oral cytology samples resulting in a “continuous risk score”. Model development consisted of: (1) training base classification models for each diagnostic class pair, (2) pairwise coupling to obtain diagnostic class probabilities, and (3) a weighted aggregation resulting in a continuous MARIO. Results and conclusions Diagnostic accuracy based on optimized cut-points for the test dataset ranged from 76.0% for Benign, to 82.4% for Dysplastic, 89.6% for Malignant, and 97.6% for Normal controls for an overall MARIO accuracy of 72.8%. Furthermore, a strong positive relationship with diagnostic severity was demonstrated (Pearson’s coefficient = 0.805 for test dataset) as well as the ability of the MARIO to respond to subtle changes in cell composition. The development of a continuous MARIO for PMOL is presented, resulting in a sensitive, accurate, and non-invasive method with potential for enabling monitoring disease progression, recurrence, and the need for therapeutic intervention of these lesions

A Multi-Stage Classifier for Face Recognition Undertaken by Coarse-to-fine Strategy

Face recognition has been a very active research area for past two decades due to its widely applications such as identity authentication, airport security and access control, surveillance, and video retrieval systems, etc. Numerous approaches have been proposed for face recognition and considerable successes have been reported [1]. A successful face recognitio

Improving Environment Detection by Behaviour Association for Context Adaptive Navigation

Navigation and positioning systems depend on both the operating environment and the behavior of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning, and the behavior can contribute additional information to the navigation solution. In order to operate across different contexts, a context‐adaptive navigation solution is required to detect the operating contexts and adopt different positioning techniques accordingly. This paper focuses on determining both environments and behaviors from smartphone sensors, serving for a context‐adaptive navigation system. Behavioral contexts cover both human activities and vehicle motions. The performance of behavior recognition in this paper is improved by feature selection and a connectivity‐dependent filter. Environmental contexts are detected from global navigation satellite system (GNSS) measurements. They are detected by using a probabilistic support vector machine, followed by a hidden Markov model for time‐domain filtering. The paper further investigates how behaviors can assist within the processes of environment detection. Finally, the proposed context‐determination algorithms are tested in a series of multicontext scenarios, showing that the proposed context association mechanism can effectively improve the accuracy of environment detection to more than 95% for pedestrian and more than 90% for vehicle

Context Detection for Advanced Self-Aware Navigation using Smartphone Sensors

Navigation and positioning systems dependent on both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning and the behaviour can contribute additional information to the navigation solution. In order to operate across different contexts, a context-adaptive navigation solution introduces an element of self-awareness by detecting the operating context and configuring the positioning system accordingly. This paper presents the detection of both environmental and behavioural contexts as a whole, building the foundation of a context-adaptive navigation system. Behavioural contexts are classified using measurements from accelerometers, gyroscopes, magnetometers and the barometer by supervised machine learning algorithms, yielding an overall 95% classification accuracy. A connectivity dependent filter is then implemented to improve the behavioural detection results. Environmental contexts are detected from GNSS measurements. They are classified into indoor, intermediate and outdoor categories using a probabilistic support vector machine (SVM), followed by a hidden Markov model (HMM) used for time-domain filtering. As there will never be completely reliable context detection, the paper also shows how environment and behaviour association can contribute to reducing the chances of the context determination algorithms selecting an incorrect context. Finally, the proposed contextdetermination algorithms are tested in a series of multi-context scenarios

Position representations of moving objects align with real-time position in the early visual response

When interacting with the dynamic world, the brain receives outdated sensory information, due to the time required for neural transmission and processing. In motion perception, the brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing

Ses sinyallerinde duygu tanıma ve geri erişimi

Ses sinyalinde duygu tanıma özelikle, görsel bilginin kısıtlı ya da hiç olmadığı durumlarda önem kazanmaktadır. Bu çalışmada, tam ve genişletilebilir bir ses tabanlı duygu tanıma ve geri erişim çatısı önerilmiştir. Makine öğrenme yöntemi olarak Destek Vektör Makineleri (DVM) kullanılmış ve performansını artırmak amacıyla parametre optimizasyonu gerçekleştirilmiştir. Ses içerik analizlerinde, uygun pencere ve atlama sürelerine karar verebilmek için ampirik analizler gerçekleştirilmiştir. Çalışmada, gürbüz öznitelikler bulmak amacıyla, 20 ses özniteliği üzerinde, DVM kullanılarak kapsamlı analizler yapılmış ve sonuçlar değerlendirilmiştir. Ayrıca, ses sinyallerinin duygu-tabanlı geri erişimi için, nokta, aralık ve en yakın komşuluk olarak adlandırılan sorgu türleri geliştirilmiş ve geri erişim başarımları değerlendirilmiştir. Deneysel sonuçlara göre, sınıflandırıcı parametre optimizasyonu ve önerilen ses analiz yöntemleri, dayanak tanıma başarımlarını arttırmaktadır. Emotion recognition from audio signals become more of significance especially when visual information is limited or absent. In this study, a complete and extensible audio-based emotion recognition and retrieval framework is proposed. Support Vector Machine (SVM) is employed as the machine learning scheme and parameter optimization methods are carried out to improve the performance of the learner. In audio content analysis, empirical analyses are performed to decide the proper window and hop sizes. In the study, extensive analyses are conducted using 20 audio features with SVM classifier to determine robust audio features and to evaluate the results. In addition, flexible querying abilities, namely point, range, and nearest neighbor are developed and retrieval performance is evaluated for emotion-based retrieval of audio signals. Based on the experiments, parameter optimization of the classifier along with the proposed audio analysis methods improve the baseline recognition accuracy

Reduced hyperBF networks : practical optimization, regularization, and applications in bioinformatics.

A hyper basis function network (HyperBF) is a generalized radial basis function network (RBF) where the activation function is a radial function of a weighted distance. The local weighting of the distance accounts for the variation in local scaling and discriminative power along each feature. Such generalization makes HyperBF networks capable of interpolating decision functions with high accuracy. However, such complexity makes HyperBF networks susceptible to overfitting. Moreover, training a HyperBF network demands weights, centers and local scaling factors to be optimized simultaneously. In the case of a relatively large dataset with a large network structure, such optimization becomes computationally challenging. In this work, a new regularization method that performs soft local dimension reduction and weight decay is presented. The regularized HyperBF (Reduced HyperBF) network is shown to provide classification accuracy comparable to a Support Vector Machines (SVM) while requiring a significantly smaller network structure. Furthermore, the soft local dimension reduction is shown to be informative for ranking features based on their localized discriminative power. In addition, a practical training approach for constructing HyperBF networks is presented. This approach uses hierarchal clustering to initialize neurons followed by a gradient optimization using a scaled Rprop algorithm with a localized partial backtracking step (iSRprop). Experimental results on a number of datasets show a faster and smoother convergence than the regular Rprop algorithm. The proposed Reduced HyperBF network is applied to two problems in bioinformatics. The first is the detection of transcription start sites (TSS) in human DNA. A novel method for improving the accuracy of TSS recognition for recently published methods is proposed. This method incorporates a new metric feature based on oligonucleotide positional frequencies. The second application is the accurate classification of microarray samples. A new feature selection algorithm based on a Reduced HyperBF network is proposed. The method is applied to two microarray datasets and is shown to select a minimal subset of features with high discriminative information. The algorithm is compared to two widely used methods and is shown to provide competitive results. In both applications, the final Reduced HyperBF network is used for higher level analysis. Significant neurons can indicate subpopulations, while local active features provide insight into the characteristics of the subpopulation in specific and the whole class in general