The Andes, along with the Amazon and Atlantic forests, harbor the richest avifauna in the world with roughly one third of all the world's species of birds. Many biogeographical studies have sought to explain the origin and diversification of Andean taxa. However, because of the Andes' extensive latitudinal span and complexity, there is no one single cause of origin or of diversification that can explain the diversity found in them. Along the Andes, multiple biogeographic patterns of disjunction between highland and lowland sister-groups have been linked to Andean uplift. For example, Ribas et al. (2007) provided evidence that the spatio-temporal diversification in the monophyletic parrot genus Pionus is causally linked to Andean tectonic and palaeoclimate change through vicariance. Thus, if the Andes uplift is responsible for some of the patterns of montane-lowland disjunctions, it may be one of the mechanisms underlying the taxonomic assembly of the Andean montane avifauna. In this dissertation I explored whether the origin and diversification of three groups of Andean birds--the exclusively Andean parrot genera Hapalopsittaca, the subclade of mangoes containing Doryfera, Schistes, and Colibri, and the ovenbirds of the tribe Thripophagini--can be linked to Earth history. The results show that the origin of these Andean taxa can be explained through vicariance from their lowland sister-groups, mediated by the uplift of the Andes. Thus, this thesis proposes that geological events are directly responsible for originating diversity throughout montane environments. Once in the Andes, the diversification of these montane taxa can be explained by events such as the tectonic evolution of the Andes--which created canyons and valleys that may have caused the vicariance of continuous populations--as well as by the climatic oscillation of the Pleistocene, which caused altitudinal shifts, expansion, and contraction of the montane vegetation belts during the climatic oscillations of the Pleistocene.

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As our society becomes more complex, it follows that the laws governing this society become more numerous and more complicated. Problems arise relative to local, state, and federal control, necessitating further interpretation and ultimately new laws. Th e areas where legal confusion is most obvious are those subject to control by all levels of government: local, state, and federal.

Les méthodes d'analyse de données et d'apprentissage ont connu un développement très important ces dernières années. En effet, après les réseaux de neurones, les machines à noyaux (années 1990), les années 2000 ont vu l'apparition de méthodes spectrales qui ont fourni un cadre mathématique unifié pour développer des méthodes de classification originales. Parmi celles-ci ont peut citer la méthode LLE pour la réduction de dimension non linéaire et la méthode LDA pour la discrimination de classes. Une nouvelle méthode de classification est proposée dans cette thèse, méthode issue d'une combinaison des méthodes LLE et LDA. Cette méthode a donné des résultats intéressants sur des ensembles de données synthétiques. Elle permet une réduction de dimension non-linéaire suivie d'une discrimination efficace. Ensuite nous avons montré que cette méthode pouvait être étendue à l'apprentissage semi-supervisé. Les propriétés de réduction de dimension et de discrimination de cette nouvelle méthode, ainsi que la propriété de parcimonie inhérente à la méthode LLE nous ont permis de l'appliquer à la segmentation d'images couleur avec succès. La propriété d'apprentissage semi-supervisé nous a enfin permis de segmenter des images bruitées avec de bonnes performances. Ces résultats doivent être confortés mais nous pouvons d'ores et déjà dégager des perspectives de poursuite de travaux intéressantes. / Data analysis and learning methods have known a huge development during these last years. Indeed, after neural networks, kernel methods in the 90', spectral methods appeared in the years 2000. Spectral methods provide an unified mathematical framework to expand new original classification methods. Among these new techniques, two methods can be highlighted : LLE for non-linear dimension reduction and LDA as discriminating classification method. In this thesis document a new classification technique is proposed combining LLE and LDA methods. This new method makes it possible to provide efficient non-linear dimension reduction and discrimination. Then an extension of the method to semi-supervised learning is proposed. Good properties of dimension reduction and discrimination associated with the sparsity property of the LLE technique make it possible to apply our method to color images segmentation with success. Semi-supervised version of our method leads to efficient segmentation of noisy color images. These results have to be extended and compared with other state-of-the-art methods. Nevertheless interesting perspectives of this work are proposed in conclusion for future developments.

This thesis addresses the problem of classifying an audio stream as either speech or music, an issue which is beginning to receive increasing attention due to its wide range of applications. Various techniques have been presented in last decade to discriminate between speech and music. However, their accuracy is still not sufficient since music can refer to a very broad class of signals due to the large number of musical instruments found in audio data. Performance can also be further compromised in noisy conditions, which are unavoidable in some practical situations. This thesis presents an analysis of feature extraction techniques and classifiers currently being used, followed by the proposal and evaluation of new features for improved classification. These include two novel cepstral features, delta cepstral energy and power spectrum deviation, along with amplitude and frequency modulation features. The modified group delay feature, initially proposed for speech recognition, is also investigated for speech and music discrimination. Experiments were performed using different sets of features, compared among themselves and with conventional MFCCs using error rate criteria and Detection Error Trade-off curves. It is shown that the proposed cepstral and modulation features result in an increase in the accuracy of the conventional MFCC based system. However, the modified group delay feature which has been shown to improve accuracy for speech classification problems, does not contribute much to the problem of speech and music discrimination. Among the ones presented here the optimum feature configuration, both modulation features with MFCC, resulted in overall error rate of 6.57% as compared to 7.43% for MFCC alone.

This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features.

This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features.

The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components.

Because of the explosion of digital and online text information, automatic organization of documents has become a very important research area. There are mainly two machine learning approaches to enhance the organization task of the digital documents. One of them is the supervised approach, where pre-defined category labels are assigned to documents based on the likelihood suggested by a training set of labeled documents; and the other one is the unsupervised approach, where there is no need for human intervention or labeled documents at any point in the whole process. In this thesis, we concentrate on the supervised learning task which deals with document classification. One of the most important tasks of information retrieval is to induce classifiers capable of categorizing text documents. The same document can belong to two or more categories and this situation is referred by the term multi-label classification. Multi-label classification domains have been encountered in diverse fields. Most of the existing machine learning techniques which are in multi-label classification domains are extremely expensive since the documents are characterized by an extremely large number of features. In this thesis, we are trying to reduce these computational costs by applying different types of algorithms to the documents which are characterized by large number of features. Another important thing that we deal in this thesis is to have the highest possible accuracy when we have the high computational performance on text document categorization.

We propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.