2,143 research outputs found
Towards learning free naive bayes nearest neighbor-based domain adaptation
As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015
Graph-based Estimation of Information Divergence Functions
abstract: Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is rarely the case in real-word scenarios. Non-parametric density estimators are characterized by a very large number of parameters that have to be tuned with costly cross-validation. In this dissertation we focus on a specific family of non-parametric estimators, called direct estimators, that bypass density estimation completely and directly estimate the quantity of interest from the data. We introduce a new divergence measure, the -divergence, that can be estimated directly from samples without parametric assumptions on the distribution. We show that the -divergence bounds the binary, cross-domain, and multi-class Bayes error rates and, in certain cases, provides provably tighter bounds than the Hellinger divergence. In addition, we also propose a new methodology that allows the experimenter to construct direct estimators for existing divergence measures or to construct new divergence measures with custom properties that are tailored to the application. To examine the practical efficacy of these new methods, we evaluate them in a statistical learning framework on a series of real-world data science problems involving speech-based monitoring of neuro-motor disorders.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Reconstrução e classificação de sequências de ADN desconhecidas
The continuous advances in DNA sequencing technologies and techniques
in metagenomics require reliable reconstruction and accurate classification
methodologies for the diversity increase of the natural repository while contributing
to the organisms' description and organization. However, after
sequencing and de-novo assembly, one of the highest complex challenges
comes from the DNA sequences that do not match or resemble any biological
sequence from the literature. Three main reasons contribute to this
exception: the organism sequence presents high divergence according to the
known organisms from the literature, an irregularity has been created in the
reconstruction process, or a new organism has been sequenced. The inability
to efficiently classify these unknown sequences increases the sample
constitution's uncertainty and becomes a wasted opportunity to discover
new species since they are often discarded.
In this context, the main objective of this thesis is the development and
validation of a tool that provides an efficient computational solution to
solve these three challenges based on an ensemble of experts, namely
compression-based predictors, the distribution of sequence content, and
normalized sequence lengths. The method uses both DNA and amino acid
sequences and provides efficient classification beyond standard referential
comparisons. Unusually, it classifies DNA sequences without resorting directly
to the reference genomes but rather to features that the species biological
sequences share. Specifically, it only makes use of features extracted
individually from each genome without using sequence comparisons.
RFSC was then created as a machine learning classification pipeline that
relies on an ensemble of experts to provide efficient classification in metagenomic
contexts. This pipeline was tested in synthetic and real data, both
achieving precise and accurate results that, at the time of the development
of this thesis, have not been reported in the state-of-the-art. Specifically, it
has achieved an accuracy of approximately 97% in the domain/type classification.Os contÃnuos avanços em tecnologias de sequenciação de ADN e técnicas
em meta genómica requerem metodologias de reconstrução confiáveis e de
classificação precisas para o aumento da diversidade do repositório natural,
contribuindo, entretanto, para a descrição e organização dos organismos.
No entanto, após a sequenciação e a montagem de-novo, um dos desafios
mais complexos advém das sequências de ADN que não correspondem ou se
assemelham a qualquer sequencia biológica da literatura. São três as principais
razões que contribuem para essa exceção: uma irregularidade emergiu
no processo de reconstrução, a sequência do organismo é altamente dissimilar
dos organismos da literatura, ou um novo e diferente organismo foi
reconstruÃdo. A incapacidade de classificar com eficiência essas sequências
desconhecidas aumenta a incerteza da constituição da amostra e desperdiça
a oportunidade de descobrir novas espécies, uma vez que muitas vezes são
descartadas.
Neste contexto, o principal objetivo desta tese é fornecer uma solução computacional
eficiente para resolver este desafio com base em um conjunto
de especialistas, nomeadamente preditores baseados em compressão, a distribuição de conteúdo de sequência e comprimentos de sequência normalizados.
O método usa sequências de ADN e de aminoácidos e fornece classificação eficiente além das comparações referenciais padrão. Excecionalmente,
ele classifica as sequências de ADN sem recorrer diretamente a genomas
de referência, mas sim à s caracterÃsticas que as sequências biológicas da
espécie compartilham. Especificamente, ele usa apenas recursos extraÃdos
individualmente de cada genoma sem usar comparações de sequência. Além
disso, o pipeline é totalmente automático e permite a reconstrução sem referência de genomas a partir de reads FASTQ com a garantia adicional de
armazenamento seguro de informações sensÃveis.
O RFSC é então um pipeline de classificação de aprendizagem automática
que se baseia em um conjunto de especialistas para fornecer classificação
eficiente em contextos meta genómicos. Este pipeline foi aplicado em dados
sintéticos e reais, alcançando em ambos resultados precisos e exatos que,
no momento do desenvolvimento desta dissertação, não foram relatados na
literatura. Especificamente, esta ferramenta desenvolvida, alcançou uma
precisão de aproximadamente 97% na classificação de domÃnio/tipo.Mestrado em Engenharia de Computadores e Telemátic
Text categorization methods for automatic estimation of verbal intelligence
In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained
Previsão Inteligente das alterações metabólicas no cancro retal com base em modelos de machine e deep learning
Machine learning, broadly speaking, applies statistical methods to training data to automatically adjust the parameters of a model, rather than a programmer needing to set them manually. Deep Learning is a sub-area of Machine Learning that studies how to solve complex and intuitive problems. The methodologies adopted, using computational means, such as the machines learned and those understood in the world in specific contexts from previous experiences and based on the hierarchy of concepts, use the most used concepts for the form and efficient solution of more varied complex problems. The main objective in this work is to study various classification algorithms in the area of machine learning, and validate until these points can use a solution for choosing more accurate methods in the selection of tests and in new statistics to improve the therapeutic response. The data involved in the training of classification algorithms refer to all patients with metabolic diseases shredding between the years 2003-2021 and the retrospective part. The best classification algorithms to develop are used in the decision support system in the most effective way in choosing the appropriate therapy for each of the future patients who predicted an approximate rate of 20 patients per year.Machine Learning, em termos gerais, aplica métodos estatÃsticos aos dados de treino para ajustar automaticamente os parâmetros de um modelo, em vez de um programador necessitar de defini-los manualmente. Deep Learning é uma subárea de Machine Learning que estuda como solucionar problemas complexos e intuitivos. As metodologias propostas permitem, com recurso a meios computacionais, que as máquinas aprendam e compreendam o mundo em determinados contextos a partir de experiências anteriores e com base na hierarquia de conceitos possam compreender conceitos mais complexos de forma a solucionarem eficientemente A mais variadÃssima gama de problemas. O principal objetivo neste trabalho consiste no estudo de vários algoritmos de classificação na área de machine learning de forma a validar até que ponto estes podem representar uma solução para a escolha de métodos mais precisos na selecção dos doentes e em novas estratégias para melhorar a resposta terapêutica. Os dados envolvidos para treino dos algoritmos de classificação referem-se a todos os doentes tratados com doenças metabólicas entre os anos 2003-2021 na parte retrospectiva. Os melhores algoritmos de classificação a desenvolver serão usados num sistema de apoio à decisão que ajude de forma mais efetiva na escolha da terapia adequada para cada um dos futuros pacientes que se prevê surgirem a uma taxa aproximada de 20 pacientes por ano
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions
Optimization of distributions differences for classification
In this paper we introduce a new classification algorithm called Optimization
of Distributions Differences (ODD). The algorithm aims to find a transformation
from the feature space to a new space where the instances in the same class are
as close as possible to one another while the gravity centers of these classes
are as far as possible from one another. This aim is formulated as a
multiobjective optimization problem that is solved by a hybrid of an
evolutionary strategy and the Quasi-Newton method. The choice of the
transformation function is flexible and could be any continuous space function.
We experiment with a linear and a non-linear transformation in this paper. We
show that the algorithm can outperform 6 other state-of-the-art classification
methods, namely naive Bayes, support vector machines, linear discriminant
analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in
12 standard classification datasets. Our results show that the method is less
sensitive to the imbalanced number of instances comparing to these methods. We
also show that ODD maintains its performance better than other classification
methods in these datasets, hence, offers a better generalization ability
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