1,722,277 research outputs found
On use of different feature sets for pattern classification: An alternative method
No full textWe propose an alternative method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g. combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets. A modular neural network architecture is proposed to implement the idea accordingly. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation. Moreover, we propose a heuristic model selection method to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.EI
Speaker identification based on the time-delay Hierarchical Mixture of Experts
No full textIn this paper, we explore the Hierarchical Mixture of Experts (HME) architecture for a substantial problem, that of text-dependent speaker identification. For a specific multi-way classification, we propose a generalized Bernolli density instead of the multinomial logit density. Time-delay technique is also introduced to HME for spatio-temporal processing. Using the proposed density and the time-delay HME along with the EM algorithm, we show that the system has a satisfactory performance and yields significantly fast training
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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