Multi Class Classification
Mostrando 1-12 de 15 artigos, teses e dissertações.
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1. Abordagens para aprendizado semissupervisionado multirrótulo e hierárquico / Multi-label and hierarchical semi-supervised learning approaches
In machine learning, the task of classification consists on creating computational models that are able to automatically identify the class of objects belonging to a predefined domain from a set of examples whose class is known a priori. There are some classification scenarios in which each object can be associated to more than one class at the same time. Mo
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 25/10/2011
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2. Classificação de dados estacionários e não estacionários baseada em grafos / Graph-based classification for stationary and non-stationary data
Métodos baseados em grafos consistem em uma poderosa forma de representação e abstração de dados que proporcionam, dentre outras vantagens, representar relações topológicas, visualizar estruturas, representar grupos de dados com formatos distintos, bem como, fornecer medidas alternativas para caracterizar os dados. Esse tipo de abordagem tem sido cad
Publicado em: 2011
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3. Periodic boundary value problems for impulsive neutral differential equations with multi-deviation arguments
We develop the impulsive inequality and the classical lower and upper solutions, and establish the comparison principles. By using these results and the monotone iterative technique, we obtain the existence of solutions of periodic boundary value problems for a class of impulsive neutral differential equations with multi-deviation arguments. An example is gi
Computational & Applied Mathematics. Publicado em: 2010
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4. Explorando master-classes de violão em festivais de música: um estudo multi-casos sobre estratégias de ensino.
The present work studies master-classes in guitar. This musical instrument teaching format is defined and its particularities are delineated with the purpose of mapping the relation between the students performance and the teaching strategies adopted by the instructor-musician. The current study aspires to answer the following question: What are and how are
Publicado em: 2010
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5. Classifiers and machine learning techniques for image processing and computer vision / Classificadores e aprendizado em processamento de imagens e visão computacional
In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images,
Publicado em: 2009
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6. UMA ABORDAGEM BASEADA EM CONHECIMENTO PARA A INTERPRETAÇÃO AUTOMÁTICA DE DADOS DE SENSORIAMENTO REMOTO MULTI-DATA / A KNOWLEDGE-BASED APPROACH FOR AUTOMATIC INTERPRETATION OF MULTIDATE REMOTE SENSING DATA
The general objective of this research was the development of knowledgebased computational techniques to support the interpretation of multitemporal remote sensing data, focusing on the investigation of the explicit representation of temporal knowledge and its integration to other types of knowledge; and also on the processing and acquisition of temporal kno
Publicado em: 2009
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7. A machine learning approach to automatic music genre classification
This paper presents a non-conventional approach for the automatic music genre classification problem. The proposed approach uses multiple feature vectors and a pattern recognition ensemble approach, according to space and time decomposition schemes. Despite being music genre classification a multi-class problem, we accomplish the task using a set of binary c
Journal of the Brazilian Computer Society. Publicado em: 2008-09
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8. FUZZY RULES EXTRACTION FROM SUPPORT VECTOR MACHINES (SVM) FOR MULTI-CLASS CLASSIFICATION / EXTRAÇÃO DE REGRAS FUZZY PARA MÁQUINAS DE VETOR SUPORTE (SVM) PARA CLASSIFICAÇÃO EM MÚLTIPLAS CLASSES
This text proposes a new method for fuzzy rule extraction from support vector machines (SVMs) trained to solve classification problems. SVMs are learning systems based on statistical learning theory and present good ability of generalization in real data base sets. These systems have been successfully applied to a wide variety of application. However SVMs, a
Publicado em: 2006
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9. Reconhecimento de padrões proteômicos e genômicos por aprendizagem de máquinas para o disgnóstico médico. / Employ machine learning to unveil encrypted molecular patterns within proteomic and genomic profiles to assist in personalized medical diagnosis.
Motivation: Employ machine learning to unveil encrypted molecular patterns within proteomic and genomic profiles to assist in personalized medical diagnosis. Results and conclusions: 1. Proteomic profile studies: Patients with Hodgkins disease (HD), a rare type of lymphoma, had their serum proteomic profile compared to control subjects (CS) in order to searc
Publicado em: 2005
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10. Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas
Atualmente, a classificação estrutural de proteínas, que diz respeito à inferência de padrões em sua conformação 3D, é um dos principais problemas em aberto da Biologia Molecular. Esse problema vem recebendo a atenção de muitos pesquisadores na área de Bioinformática pelo fato de as funções das proteínas estarem intrinsecamente relacionadas �
Publicado em: 2005
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11. A DATA MODEL FOR EXPLORATION OF TEMPORAL VIRTUAL REALITY GEOGRAPHIC INFORMATION SYSTEMS
Geographic information systems deal with the exploration, analysis, and presentation of geo-referenced data. Virtual reality is a type of human-computer interface that comes close to the way people perceive information in the real world. Thus, virtual reality environments become the natural paradigm for extending and enhancing the presentational and explorat
Publicado em: 2004
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12. ProClass Protein Family Database.
ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PROSITE patterns and PIR superfamilies. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database