67,020 research outputs found
Model fusion using fuzzy aggregation: Special applications to metal properties
To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary
Meta-Feature (FSL-BM) for big data classification task. The study of real-time
algorithms addresses several major concerns, which are namely: accuracy, memory
consumption, and ability to stretch assumptions and time complexity. Attaining
a fast computational model providing fuzzy logic and supervised learning is one
of the main challenges in the machine learning. In this research paper, we
present FSL-BM algorithm as an efficient solution of supervised learning with
fuzzy logic processing using binary meta-feature representation using Hamming
Distance and Hash function to relax assumptions. While many studies focused on
reducing time complexity and increasing accuracy during the last decade, the
novel contribution of this proposed solution comes through integration of
Hamming Distance, Hash function, binary meta-features, binary classification to
provide real time supervised method. Hash Tables (HT) component gives a fast
access to existing indices; and therefore, the generation of new indices in a
constant time complexity, which supersedes existing fuzzy supervised algorithms
with better or comparable results. To summarize, the main contribution of this
technique for real-time Fuzzy Supervised Learning is to represent hypothesis
through binary input as meta-feature space and creating the Fuzzy Supervised
Hash table to train and validate model.Comment: FICC201
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
Machine learning stochastic design models.
Due to the fluid nature of the early stages of the design process, it is difficult to obtain deterministic product design evaluations. This is primarily due to the flexibility of the design at this stage, namely that there can be multiple interpretations of a single design concept. However, it is important for designers to understand how these design concepts are likely to fulfil the original specification, thus enabling the designer to select or bias towards solutions with favourable outcomes. One approach is to create a stochastic model of the design domain. This paper tackles the issues of using a product database to induce a Bayesian model that represents the relationships between the design parameters and characteristics. A greedy learning algorithm is presented and illustrated using a simple case study
A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks
Recently much attention has been devoted to the construction of phylogenetic
networks which generalize phylogenetic trees in order to accommodate complex
evolutionary processes. Here we present an efficient, practical algorithm for
reconstructing level-1 phylogenetic networks - a type of network slightly more
general than a phylogenetic tree - from triplets. Our algorithm has been made
publicly available as the program LEV1ATHAN. It combines ideas from several
known theoretical algorithms for phylogenetic tree and network reconstruction
with two novel subroutines. Namely, an exponential-time exact and a greedy
algorithm both of which are of independent theoretical interest. Most
importantly, LEV1ATHAN runs in polynomial time and always constructs a level-1
network. If the data is consistent with a phylogenetic tree, then the algorithm
constructs such a tree. Moreover, if the input triplet set is dense and, in
addition, is fully consistent with some level-1 network, it will find such a
network. The potential of LEV1ATHAN is explored by means of an extensive
simulation study and a biological data set. One of our conclusions is that
LEV1ATHAN is able to construct networks consistent with a high percentage of
input triplets, even when these input triplets are affected by a low to
moderate level of noise
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