1,720,984 research outputs found
Run-Time Performance Analysis of the Mixture of Experts Model
No full textThe Mixture of Experts (ME) model is one of the most popular ensemble methods used in pattern recognition and machine learning. Despite many studies on the theory and application of the ME model, to our knowledge, its training, testing, and evaluation costs have not been investigated yet. After analyzing the ME model in terms of number of required floating point operations, this paper makes an experimental comparison between the ME model and the recently proposed Mixture of Random Prototype Experts. Experiments have been performed on selected datasets from the UCI machine learning repository. Experimental results confirm the expected behavior of the two ME models, while highlighting that the latter performs better in terms of accuracy and run-time performance
Test infrastructures evaluation at transaction level
Full text linkThe goal of this work is to propose a method to fully exploit TLM2.0 potentialities to evaluate test infrastructures. By providing the high level model with necessary information from RTL, the behavior of test infrastructures can be simulated taking advantage of high simulation speed of TLM. This way, the high level model is able to both estimate the cost of test infrastructure much faster and facilitate decision making for proper test infrastructure at RTL
An improved mixture of experts model: Divide and conquer using random prototypes
No full textThe Mixture of Experts (ME) is one of the most popular ensemble methods used in Pattern Recognition and Machine Learning. This algorithm stochastically partitions the input space of a problem into a number of subspaces, experts becoming specialized on each subspace. To manage this process, theME uses an expert called gating network, which is trained together with the other experts. In this chapter, we propose a modified version of the ME algorithm which first partitions the original problem into centralized regions and then uses a simple distance-based gating function to specialize the expert networks. Each expert contributes to classify an input sample according to the distance between the input and a prototype embedded by the expert. The Hierarchical Mixture of Experts (HME) is a tree-structured architecture which can be considered a natural extension of the ME model. The training and testing strategies of the standard HME model are also modified, based on the same insight applied to standard ME. In both cases, the proposed approach does not require to train the gating networks, as they are implemented with simple distance-based rules. In so doing the overall time required for training a modifiedME/ HME system is considerably lower. Moreover, centralizing input subspaces and adopting a random strategy for selecting prototypes permits to increase at the same time individual accuracy and diversity of ME/HME modules, which in turn increases the accuracy of the overall ensemble. Experimental results on a binary toy problem and on selected datasets from the UCI machine learning repository show the robustness of the proposed methods compared to the standard ME/HM
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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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