En classification multi-labels graduée (CMLG), chaque instance est associée à un ensemble de labels avec des degrés d’association gradués. Par exemple, une même molécule odorante peut être associée à une odeur forte ‘musquée’, une odeur modérée ‘animale’, et une odeur faible ‘herbacée’. L’objectif est d’apprendre un modèle permettant de prédire l’ensemble gradué de labels associé à une instance à partir de ses variables descriptives. Par exemple, prédire l’ensemble gradué d’odeurs à partir de la masse moléculaire, du nombre de liaisons doubles, et de la structure de la molécule. Un autre domaine intéressant de la CMLG est les systèmes de recommandation. En effet, les appréciations des utilisateurs par rapport à des items (produits, services, livres, films, etc) sont d’abord collectées sous forme de données MLG (l’échelle d’une à cinq étoiles est souvent utilisée). Ces données sont ensuite exploitées pour recommander à chaque utilisateur des items qui ont le plus de chance de l’intéresser. Dans cette thèse, une étude théorique approfondie de la CMLG permet de ressortir les limites des approches existantes, et d’assoir un ensemble de nouvelles approches apportant des améliorations évaluées expérimentalement sur des données réelles. Le cœur des nouvelles approches proposées est l’exploitation des relations entre les labels. Par exemple, une molécule ayant une forte odeur ‘musquée’ émet souvent une odeur faible ou modérée ‘animale’. Cette thèse propose également de nouvelles approches adaptées au cas des molécules odorantes et au cas des gros volumes de données collectées dans le cadre des systèmes de recommandation. / In graded multi-label classification (GMLC), each instance is associated to a set of labels with graded membership degrees. For example, the same odorous molecule may be associated to a strong 'musky' odor, a moderate 'animal' odor, and a weak 'grassy' odor. The goal is to learn a model to predict the graded set of labels associated to an instance from its descriptive variables. For example, predict the graduated set of odors from the molecular weight, the number of double bonds, and the structure of the molecule. Another interesting area of the GMLC is recommendation systems. In fact, users' assessments of items (products, services, books, films, etc.) are first collected in the form of GML data (using the one-to-five star rating). These data are then used to recommend to each user items that are most likely to interest him. In this thesis, an in-depth theoretical study of the GMLC allows to highlight the limits of existing approaches, and to introduce a set of new approaches bringing improvements evaluated experimentally on real data. The main point of the new proposed approaches is the exploitation of relations between labels. For example, a molecule with a strong 'musky' odor often has a weak or moderate 'animal' odor. This thesis also proposes new approaches adapted to the case of odorous molecules and to the case of large volumes of data collected in the context of recommendation systems.

No description available.

Transformers have shown remarkable results in the domains of Natural Language Processing and Computer Vision. This naturally raises the question whether the success could be replicated in other domains. However, due to Transformers being inherently data hungry and sensitive to weight initialization, applying the Transformer to new domains is quite a challenging task. Previously, the data demands have been met using large scale supervised or self-supervised pre-training on a similar task before supervised fine-tuning on a target down stream task. We show that Transformers are applicable for the task of multi-label error classification of trace data, and that masked data modelling based self-supervised learning methods can be used to leverage unlabelled data to increase performance compared to a baseline supervised learning approach. / Transformers har visat upp anmärkningsvärda resultat inom områdena Natural Language Processing och Computer Vision. Detta väcker naturligtvis frågan om dessa framgångar kan upprepas inom andra områden. På grund av att transformatorer i sig är datahungriga och känsliga för initialisering av vikt är det dock en utmaning att tillämpa transformatorn på nya områden. Tidigare har datakraven tillgodosetts med hjälp av storskalig övervakad eller självövervakad förträning på en liknande uppgift före övervakad finjustering på en måluppgift i efterföljande led. Vi visar att transformatorer kan användas för klassificering av spårdata med flera etiketter och att metoder för självövervakad inlärning som bygger på modellering av maskerade data kan användas för att utnyttja omärkta data för att öka prestandan jämfört med en grundläggande övervakad inlärningsmetod.

[pt] As proteínas são macromoléculas biológicas compostas por cadeias de aminoácidos, presentes em praticamente todos os processos celulares, sendo essenciais para o correto funcionamento do organismo humano. Existem diversos estudos em torno do proteoma humano a fim de se identificar quais são as funções de cada proteína nas diferentes células, tecidos e órgãos do corpo humano. A classificação destas proteínas em diferentes formas, como por exemplo a localização subcelular, é importante para diversas aplicações da biomedicina. Com o avanço das tecnologias para obtenção de imagens das proteínas, tem-se que hoje estas são geradas em grande volume e mais rapidamente do que é possível classificá-las manualmente, o que torna importante o desenvolvimento de um classificador automático capaz de realizar esta classificação de maneira eficaz. Dessa forma, esta dissertação buscou desenvolver algoritmos capazes de realizar a classificação automática de padrões mistos de localização subcelular de proteínas, por meio do uso de técnicas de Deep Learning. Inicialmente, fez-se uma revisão da literatura em torno de redes neurais, Deep Learning e SVMs, e utilizou-se o banco de dados, publicamente disponíve, de imagens de células do Human Protein Atlas, para treinamento dos algoritmos de aprendizagem supervisionada. Diversos modelos foram desenvolvidos e avaliados, visando identificar aquele com melhor desempenho na tarefa de classificação. Ao longo do trabalho foram desenvolvidas redes neurais artificiais convolucionais de topologia LeNet, ResNet e um modelo híbrido ResNet-SVM, tendo sido treinadas ao todo 81 redes neurais diferentes, a fim de se identificar o melhor conjunto de hiper-parâmetros. As análises efetuadas permitiram concluir que a rede de melhor desempenho foi uma variante da topologia ResNet, que obteve em suas métricas de desempenho uma acurácia de 0,94 e uma pontuação F1 de 0,44 ao se avaliar o comportamento da rede frente ao conjunto de teste. Os resultados obtidos pela diferentes topologias analisadas foram detalhadamente avaliados e, com base nos resultados alcançados, foram sugeridos trabalhos futuros baseados em possíveis melhorias para as redes de melhor desempenho. / [en] Proteins are biological macromolecules composed of aminoacid chains, part of practically all cellular processes, being essential for the correct functioning of the human organism. There are many studies around the human protein aiming to identify the proteins’ functions in different cells, tissues and organs in the human body. The protein classification in many forms, such as the subcellular localization, is important for many biomedical applications. With the advance of protein image obtention technology, today these images are generated in large scale and faster than it is possible to manually classify them, which makes crucial the development of a system capable of classifying these images automatically and accurately. In that matter, this dissertation aimed to develop algorithms capable of automatically classifying proteins in mixed patterns of subcellular localization with the use of Deep Learning techniques. Initially, a literature review on neural networks, Deep Learning and SVMs, and a publicly available image database from the Human Protein Atlas was used to train the supervised learning algorithms. Many models were developed seeking the best performance in the classification task. Throughout this work, convolutional artificial neural networks of topologies LeNet, ResNet and a hybrid ResNet-SVM model were developed, with a total of 81 different neural networks trained, aiming to identify the best hyper-parameters. The analysis allowed the conclusion that the network with best performance was a ResNet variation, which obtained in its performance metrics an accuracy of 0.94 and an F1 score of 0.44 when evaluated against the test data. The obtained results of these topologies were detailedly evaluated and, based on the measured results, future studies were suggested based on possible improvements for the neural networks that had the best performances.

The emergence of large language models, such as BERT and GPT-3, has revolutionized natural language processing tasks. However, the development and deployment of these models pose challenges, including concerns about computational resources and environmental impact. This study aims to compare discriminative language models for text classification based on their performance and usage cost. We evaluate the models using a hierarchical multi-label text classification task and assess their performance using primarly F1-score. Additionally, we analyze the usage cost by calculating the Floating Point Operations (FLOPs) required for inference. We compare a baseline model, which consists of a classifier chain with logistic regression models, with fine-tuned discriminative language models, including BERT with two different sequence lengths and DistilBERT, a distilled version of BERT. Results show that the DistilBERT model performs optimally in terms of performance, achieving an F1-score of 0.56 averaged on all classification layers. The baseline model and BERT with a maximal sequence length of 128 achieve F1-scores of 0.51. However, the baseline model outperforms the transformers at the most specific classification level with an F1-score of 0.33. Regarding usage cost, the baseline model significantly requires fewer FLOPs compared to the transformers. Furthermore, restricting BERT to a maximum sequence length of 128 tokens instead of 512 sacrifices some performance but offers substantial gains in usage cost. The code and dataset are available on GitHub. / Uppkomsten av stora språkmodeller, som BERT och GPT-3, har revolutionerat språkteknologi. Dock ger utvecklingen och implementeringen av dessa modeller upphov till utmaningar, bland annat gällande beräkningsresurser och miljöpåverkan. Denna studie syftar till att jämföra diskriminativa språkmodeller för textklassificering baserat på deras prestanda och användningskostnad. Vi utvärderar modellerna genom att använda en hierarkisk textklassificeringsuppgift och bedöma deras prestanda primärt genom F1-score. Dessutom analyserar vi användningskostnaden genom att beräkna antalet flyttalsoperationer (FLOPs) som krävs för inferens. Vi jämför en grundläggande modell, som består av en klassifikationskedja med logistisk regression, med finjusterande diskriminativa språkmodeller, inklusive BERT med två olika sekvenslängder och DistilBERT, en destillerad version av BERT. Resultaten visar att DistilBERT-modellen presterar optimalt i fråga om prestanda och uppnår en genomsnittlig F1-score på 0,56 för alla klassificeringsnivåer. Den grundläggande modellen och BERT med en maximal sekvenslängd på 128 uppnår ett F1-score på 0,51. Dock överträffar den grundläggande modellen transformermodellerna på den mest specifika klassificeringsnivån med en F1-score på 0,33. När det gäller användningskostnaden kräver den grundläggande modellen betydligt färre FLOPs jämfört med transformermodellerna. Att begränsa BERT till en maximal sekvenslängd av 128 tokens ger vissa prestandaförluster men erbjuder betydande besparingar i användningskostnaden. Koden och datamängden är tillgängliga på GitHub.

This thesis describes a process of creating a mobile application using deep convolutional neural networks. The process starts with proposal of the main idea, followed by product and technical design, implementation and evaluation. The thesis also explores the technical background of image recognition, and chooses the most suitable options for the purpose of the application. These are object detection and multi-label classification, which are both implemented, evaluated and compared. The resulting application tries to bring value from both user and technical point of view.

Text Classification (TC) which is an important field in information technology has many valuable applications. When facing the sea of information resources, the objects of TC are more complicated and diversity. The researches in pursuit of effective and practical TC technology are fairly challenging. More and more researchers regard that multi-label TC is more suited for many applications. This thesis analyses the difficulties and problems in multi-label TC and Chinese text representation based on a mass of algorithms for single-label TC and multi-label TC. Aiming at high dimensionality in feature space, sparse distribution in text representation and poor performance of multi-label classifier, this thesis will bring forward corresponding algorithms from different angles.Focusing on the problem of dimensionality “disaster” when Chinese texts are represented by using n-grams, two-step feature selection algorithm is constructed. The method combines filtering rare features within class and selecting discriminative features across classes. Moreover, the proper value of “n”, the strategy of feature weight and the correlation among features are discussed based on variety of experiments. Some useful conclusions are contributed to the research of n-gram representation in Chinese texts.In a view of the disadvantage in Latent Dirichlet Allocation (LDA) model, that is, arbitrarily revising the variable in smooth process, a new strategy for smoothing based on Tolerance Rough Set (TRS) is put forward. It constructs tolerant class in global vocabulary database firstly and then assigns value for out-of-vocabulary (oov) word in each class according to tolerant class.In order to improve performance of multi-label classifier and degrade computing complexity, a new TC method based on LDA model is applied for Chinese text representation. It extracts topics statistically from texts and then texts are represented by using the topic vector. It shows competitive performance both in English and in Chinese corpus.To enhance the performance of classifiers in multi-label TC, a compound classification framework is raised. It partitions the text space by computing the upper approximation and lower approximation. This algorithm decomposes a multi-label TC problem into several single-label TCs and several multi-label TCs which have less labels than original problem. That is, an unknown text should be classified by single-label classifier when it is partitioned into lower approximation space of some class. Otherwise, it should be classified by corresponding multi-label classifier.An application system TJ-MLWC (Tongji Multi-label Web Classifier) was designed. It could call the result from Search Engines directly and classify these results real-time using improved Naïve Bayes classifier. This makes the browse process more conveniently for users. Users could locate the texts interested immediately according to the class information given by TJ-MLWC. / La thèse est centrée sur la Classification de texte, domaine en pleine expansion, avec de nombreuses applications actuelles et potentielles. Les apports principaux de la thèse portent sur deux points : Les spécificités du codage et du traitement automatique de la langue chinoise : mots pouvant être composés de un, deux ou trois caractères ; absence de séparation typographique entre les mots ; grand nombre d’ordres possibles entre les mots d’une phrase ; tout ceci aboutissant à des problèmes difficiles d’ambiguïté. La solution du codage en «n-grams »(suite de n=1, ou 2 ou 3 caractères) est particulièrement adaptée à la langue chinoise, car elle est rapide et ne nécessite pas les étapes préalables de reconnaissance des mots à l’aide d’un dictionnaire, ni leur séparation. La classification multi-labels, c'est-à-dire quand chaque individus peut être affecté à une ou plusieurs classes. Dans le cas des textes, on cherche des classes qui correspondent à des thèmes (topics) ; un même texte pouvant être rattaché à un ou plusieurs thème. Cette approche multilabel est plus générale : un même patient peut être atteint de plusieurs pathologies ; une même entreprise peut être active dans plusieurs secteurs industriels ou de services. La thèse analyse ces problèmes et tente de leur apporter des solutions, d’abord pour les classifieurs unilabels, puis multi-labels. Parmi les difficultés, la définition des variables caractérisant les textes, leur grand nombre, le traitement des tableaux creux (beaucoup de zéros dans la matrice croisant les textes et les descripteurs), et les performances relativement mauvaises des classifieurs multi-classes habituels. / 文本分类是信息科学中一个重要而且富有实际应用价值的研究领域。随着文本分类处理内容日趋复杂化和多元化，分类目标也逐渐多样化，研究有效的、切合实际应用需求的文本分类技术成为一个很有挑战性的任务，对多标签分类的研究应运而生。本文在对大量的单标签和多标签文本分类算法进行分析和研究的基础上，针对文本表示中特征高维问题、数据稀疏问题和多标签分类中分类复杂度高而精度低的问题，从不同的角度尝试运用粗糙集理论加以解决，提出了相应的算法，主要包括：针对n-gram作为中文文本特征时带来的维数灾难问题，提出了两步特征选择的方法，即去除类内稀有特征和类间特征选择相结合的方法，并就n-gram作为特征时的n值选取、特征权重的选择和特征相关性等问题在大规模中文语料库上进行了大量的实验，得出一些有用的结论。针对文本分类中运用高维特征表示文本带来的分类效率低，开销大等问题，提出了基于LDA模型的多标签文本分类算法，利用LDA模型提取的主题作为文本特征，构建高效的分类器。在PT3多标签分类转换方法下，该分类算法在中英文数据集上都表现出很好的效果，与目前公认最好的多标签分类方法效果相当。针对LDA模型现有平滑策略的随意性和武断性的缺点，提出了基于容差粗糙集的LDA语言模型平滑策略。该平滑策略首先在全局词表上构造词的容差类，再根据容差类中词的频率为每类文档的未登录词赋予平滑值。在中英文、平衡和不平衡语料库上的大量实验都表明该平滑方法显著提高了LDA模型的分类性能，在不平衡语料库上的提高尤其明显。针对多标签分类中分类复杂度高而精度低的问题，提出了一种基于可变精度粗糙集的复合多标签文本分类框架，该框架通过可变精度粗糙集方法划分文本特征空间，进而将多标签分类问题分解为若干个两类单标签分类问题和若干个标签数减少了的多标签分类问题。即，当一篇未知文本被划分到某一类文本的下近似区域时，可以直接用简单的单标签文本分类器判断其类别；当未知文本被划分在边界域时，则采用相应区域的多标签分类器进行分类。实验表明，这种分类框架下，分类的精确度和算法效率都有较大的提高。本文还设计和实现了一个基于多标签分类的网页搜索结果可视化系统（MLWC），该系统能够直接调用搜索引擎返回的搜索结果，并采用改进的Naïve Bayes多标签分类算法实现实时的搜索结果分类，使用户可以快速地定位搜索结果中感兴趣的文本。

Made available in DSpace on 2014-12-17T15:48:09Z (GMT). No. of bitstreams: 1 FillipeMR_DISSERT.pdf: 1204563 bytes, checksum: 66d7e69371d4103cf2e242609ed0bbb7 (MD5) Previous issue date: 2014-02-21 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The techniques of Machine Learning are applied in classification tasks to acquire knowledge through a set of data or information. Some learning methods proposed in literature are methods based on semissupervised learning; this is represented by small percentage of labeled data (supervised learning) combined with a quantity of label and non-labeled examples (unsupervised learning) during the training phase, which reduces, therefore, the need for a large quantity of labeled instances when only small dataset of labeled instances is available for training. A commom problem in semi-supervised learning is as random selection of instances, since most of paper use a random selection technique which can cause a negative impact. Much of machine learning methods treat single-label problems, in other words, problems where a given set of data are associated with a single class; however, through the requirement existent to classify data in a lot of domain, or more than one class, this classification as called multi-label classification. This work presents an experimental analysis of the results obtained using semissupervised learning in troubles of multi-label classification using reliability parameter as an aid in the classification data. Thus, the use of techniques of semissupervised learning and besides methods of multi-label classification, were essential to show the results / As t?cnicas de Aprendizado de M?quina s?o aplicadas em tarefas de classifica??o para a aquisi??o de conhecimento atrav?s de um conjunto de dados ou informa??es. Alguns m?todos de aprendizado utilizados pela literatura s?o baseados em aprendizado semissupervisionado; este ? representado por pequeno percentual de exemplos rotulados (aprendizado supervisionado) combinados com uma quantidade de exemplos rotulados e n?o rotulados (n?o-supervisionado) durante a fase de treinamento, reduzindo, portanto, a necessidade de uma grande quantidade de dados rotulados quando apenas um pequeno conjunto de exemplos rotulados est? dispon?vel para treinamento. O problema da escolha aleat?ria das inst?ncias ? comum no aprendizado semissupervisionado, pois a maioria dos trabalhos usam a escolha aleat?ria dessas inst?ncias o que pode causar um impacto negativo. Por outro lado, grande parte dos m?todos de aprendizado de m?quina trata de problemas unirr?tulo, ou seja, problemas onde exemplos de um determinado conjunto s?o associados a uma ?nica classe. Entretanto, diante da necessidade existente de classificar dados em uma grande quantidade de dom?nios, ou em mais de uma classe, essa classifica??o citada ? denominada classifica??o multirr?tulo. Este trabalho apresenta uma an?lise experimental dos resultados obtidos por meio da utiliza??o do aprendizado semissupervisionado em problemas de classifica??o multirr?tulo usando um par?metro de confiabilidade como aux?lio na classifica??o dos dados. Dessa maneira, a utiliza??o de t?cnicas de aprendizado semissupervisionado, bem como de m?todos de classifica??o multirr?tulos, foram imprescind?veis na apresenta??o dos resultados

In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better features, which further improves many supervised tasks. These models not only automate the feature extraction process but also provide with robust features for various machine learning tasks. But the unsupervised pretraining and feature extraction using multi-layered networks are restricted only to the input features and not to the output. The performance of many supervised learning algorithms (or models) depends on how well the output dependencies are handled by these algorithms [Dembczy´nski et al., 2012]. Adapting the standard neural networks to handle these output dependencies for any specific type of problem has been an active area of research [Zhang and Zhou, 2006, Ribeiro et al., 2012]. On the other hand, inference into multimodal data is considered as a difficult problem in machine learning and recently ‘deep multimodal neural networks’ have shown significant results [Ngiam et al., 2011, Srivastava and Salakhutdinov, 2012]. Several problems like classification with complete or missing modality data, generating the missing modality etc., are shown to perform very well with these models. In this work, we consider three nontrivial supervised learning tasks (i) multi-class classification (MCC), (ii) multi-label classification (MLC) and (iii) label ranking (LR), mentioned in the order of increasing complexity of the output. While multi-class classification deals with predicting one class for every instance, multi-label classification deals with predicting more than one classes for every instance and label ranking deals with assigning a rank to each label for every instance. All the work in this field is associated around formulating new error functions that can force network to identify the output dependencies. Aim of our work is to adapt neural network to implicitly handle the feature extraction (dependencies) for output in the network structure, removing the need of hand crafted error functions. We show that the multimodal deep architectures can be adapted for these type of problems (or data) by considering labels as one of the modalities. This also brings unsupervised pretraining to the output along with the input. We show that these models can not only outperform standard deep neural networks, but also outperform standard adaptations of neural networks for individual domains under various metrics over several data sets considered by us. We can observe that the performance of our models over other models improves even more as the complexity of the output/ problem increases.

SILVA, Ana Cristina Cunha da. O uso de redes neurais auto-organizáveis na análise da transferência de conhecimentos prosódico em aprendizes brasileiros de língua inglesa. 2010, 201f. Tese (Doutorado em Linguística) – Universidade Federal do Ceará, Departamento de Letras Vernáculas, Programa de Pós-graduação em Linguística, Fortaleza-CE, 2010. / Submitted by nazareno mesquita (nazagon36@yahoo.com.br) on 2012-06-28T13:08:58Z No. of bitstreams: 1 2010_tese_ACCSilva.pdf: 2172197 bytes, checksum: 036ba2cdc331410f0516a0ba2abe520d (MD5) / Approved for entry into archive by Maria Josineide Góis(josineide@ufc.br) on 2013-10-10T13:22:45Z (GMT) No. of bitstreams: 1 2010_tese_ACCSilva.pdf: 2172197 bytes, checksum: 036ba2cdc331410f0516a0ba2abe520d (MD5) / Made available in DSpace on 2013-10-10T13:22:45Z (GMT). No. of bitstreams: 1 2010_tese_ACCSilva.pdf: 2172197 bytes, checksum: 036ba2cdc331410f0516a0ba2abe520d (MD5) Previous issue date: 2010 / The objective of this dissertation was to investigate how the prosodic knowledge is organized in an early stage of L2 acquisition in Brazilian learners of English with the help of a connectionist neural network. The approach proposed in this research is first, to quantify the utterances of L2 learners in the form of LPC coefficients and other linguistic/phonetics features that can represent the phenomenon studied here (Transfer of the prosodic knowledge from Portuguese to English). This process is called speech feature extraction, an important step in the connectionist approach to speech processing. Second, since certain features of the lexical item or sentence produced by each learner are determined, these data are entered into the neural network to analyze the statistical properties (regularities) of the set of speakers as a whole. Third, visualization tools are used to analyze how the network organizes speakers and what information is most relevant to this process of group formation (e.g. proficiency level, a certain characteristic or property of speech, among others). The network is known as Self-Organizing Map (Self-Organizing Map, SOM). The SOM organizes speakers for similarity degree in well-defined groups (clusters). Application of SOM in this context is therefore innovative. The SOM network is implemented in Matlab environment using the SOMtoolbox package, which is a set of programming routines developed by the research group in Finland, also the inventors of the SOM. The simulation results indicate that SOM might be used more frequently to assess the degree of distance that a group of learners is to the group of native speakers. Thus, a neural network might be used as a tool in the context of determining the level of foreign language proficiency. / O objetivo desta tese foi investigar como o conhecimento prosódico está organizado em um estágio inicial de aquisição de L2 em aprendizes brasileiros de inglês com a ajuda de uma rede neural conexionista. A abordagem proposta neste trabalho consiste primeiramente em "quantificar" as elocuções dos aprendizes de L2 na forma de coeficientes LPC e outras características linguísticas/fonéticas que possam representar o fenômeno aqui estudado (Transferência do Conhecimento Prosódico do Português para o inglês). A este processo dá-se o nome de "extração de características" da fala (feature extraction), uma importante etapa na abordagem conexionista do processamento da fala. Em segundo lugar, uma vez determinadas as características do item lexical ou da frase produzida por cada aprendiz, são inseridos esses dados na rede neural a fim de analisar as propriedades (regularidades) estatísticas do conjunto de falantes como um todo. Em terceiro, utiliza-se ferramentas de visualização para analisar como a rede organiza os falantes e quais informações são mais relevantes para este processo de formação de grupos (e.g. nível de proficiência, uma certa característica ou propriedade da fala, entre outros). A rede utilizada é conhecida como Mapa Auto-Organizável (Self-Organizing Map, SOM). A rede SOM organiza os falantes por grau de similaridade em grupos bem definidos (clusters). A aplicação da rede SOM neste contexto é, portanto, inovadora. A rede SOM é implementada no ambiente Matlab usando o pacote Som toolbox, que é um conjunto de rotinas de programação desenvolvidas pelo grupo de pesquisa da Finlândia, também inventores da rede SOM. Os resultados das simulações apontam que a rede SOM pode vir a ser usada mais frequentemente para avaliar o grau de distância a que um grupo de aprendizes está do grupo de falantes nativos. Dessa forma, uma rede neural pode vir a ser aplicada como ferramenta no contexto de determinação de nível de proficiência em língua estrangeira.