After three decades of research, instructional communication scholars find themselves facing several key challenges. These range from an overemphasis in past studies on variable-analytic, atheoretical research to a lack of connection to learning outcomes. Many in the field contend that the time has come for instructional communication researchers to define instructional communication theories, test hypotheses, tie research efforts to learning outcomes, and clarify key terms. The present study addressed these shortcomings by proposing the Content Relevance Centric Theory and testing related hypotheses. The research occurred in a professional training environment and involved the use of a modified content relevance instrument that assessed both teacher communication characteristics and message content relevance. The study gathered data from 247 trainees. Results indicate the importance of the construct as a predictor of trainee behavioral intentions both directly and when mediated by both trainee state motivation and trainer credibility. Study outcomes also question the role of trainee engagement in learning and the connection between behavioral intentions and learning application.

This work investigates the self-organizing representation of temporal data in prototype-based neural networks. Extensions of the supervised learning vector quantization (LVQ) and the unsupervised self-organizing map (SOM) are considered in detail. The principle of Hebbian learning through prototypes yields compact data models that can be easily interpreted by similarity reasoning. In order to obtain a robust prototype dynamic, LVQ is extended by neighborhood cooperation between neurons to prevent a strong dependence on the initial prototype locations. Additionally, implementations of more general, adaptive metrics are studied with a particular focus on the built-in detection of data attributes involved for a given classifcation task. For unsupervised sequence processing, two modifcations of SOM are pursued: the SOM for structured data (SOMSD) realizing an efficient back-reference to the previous best matching neuron in a triangular low-dimensional neural lattice, and the merge SOM (MSOM) expressing the temporal context as a fractal combination of the previously most active neuron and its context. The first SOMSD extension tackles data dimension reduction and planar visualization, the second MSOM is designed for obtaining higher quantization accuracy. The supplied experiments underline the data modeling quality of the presented methods.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-24T15:04:03Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mapas-auto-organizaveis2.pdf: 2835656 bytes, checksum: 8836a86bd2cced9353cb25b53383b305 (MD5) / Made available in DSpace on 2017-04-24T15:04:03Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mapas-auto-organizaveis2.pdf: 2835656 bytes, checksum: 8836a86bd2cced9353cb25b53383b305 (MD5) Previous issue date: 2016-08-16 / Métodos e algoritmos em aprendizado de máquina não supervisionado têm sido empregados em diversos problemas significativos. Uma explosão na disponibilidade de dados de várias fontes e modalidades está correlacionada com os avanços na obtenção, compressão, armazenamento, transferência e processamento de grandes quantidades de dados complexos com alta dimensionalidade, como imagens digitais, vídeos de vigilância e microarranjos de DNA. O agrupamento se torna difícil devido à crescente dispersão desses dados, bem como a dificuldade crescente em discriminar distâncias entre os pontos de dados. Este trabalho apresenta um algoritmo de agrupamento suave em subespaços baseado em um mapa auto-organizável (SOM) com estrutura variante no tempo, o que significa que o agrupamento dos dados pode ser alcançado sem qualquer conhecimento prévio, tais como o número de categorias ou a topologia dos padrões de entrada, nos quais ambos são determinados durante o processo de treinamento. O modelo também atribui diferentes pesos a diferentes dimensões, o que implica que cada dimensão contribui para o descobrimento dos aglomerados de dados. Para validar o modelo, diversos conjuntos de dados reais foram utilizados, considerando uma diversificada gama de contextos, tais como mineração de dados, expressão genética, agrupamento multivista e problemas de visão computacional. Os resultados são promissores e conseguem lidar com dados reais caracterizados pela alta dimensionalidade. / Unsupervised learning methods have been employed on many significant problems. A blast in the availability of data from multiple sources and modalities is correlated with advancements in how to obtain, compress, store, transfer, and process large amounts of complex high-dimensional data, such as digital images, surveillance videos, and DNA microarrays. Clustering becomes challenging due to the increasing sparsity of such data, as well as the increasing difficulty in discriminating distances between data points. This work presents a soft subspace clustering algorithm based on a self-organizing map (SOM) with time-variant structure, meaning that clustering data can be achieved without any prior knowledge such as the number of categories or input data topology, in which both are determined during the training process. The model also assigns different weights to different dimensions, this implies that every dimension contributes to uncover clusters. To validate the model, we used a number of real-world data sets, considering a diverse range of contexts such as data mining, gene expression, multi-view and computer vision problems. The promising results can handle real-world data characterized by high dimensionality.