20,215 research outputs found
Elite Bases Regression: A Real-time Algorithm for Symbolic Regression
Symbolic regression is an important but challenging research topic in data
mining. It can detect the underlying mathematical models. Genetic programming
(GP) is one of the most popular methods for symbolic regression. However, its
convergence speed might be too slow for large scale problems with a large
number of variables. This drawback has become a bottleneck in practical
applications. In this paper, a new non-evolutionary real-time algorithm for
symbolic regression, Elite Bases Regression (EBR), is proposed. EBR generates a
set of candidate basis functions coded with parse-matrix in specific mapping
rules. Meanwhile, a certain number of elite bases are preserved and updated
iteratively according to the correlation coefficients with respect to the
target model. The regression model is then spanned by the elite bases. A
comparative study between EBR and a recent proposed machine learning method for
symbolic regression, Fast Function eXtraction (FFX), are conducted. Numerical
results indicate that EBR can solve symbolic regression problems more
effectively.Comment: The 2017 13th International Conference on Natural Computation, Fuzzy
Systems and Knowledge Discovery (ICNC-FSKD 2017
Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima
The optimization of three problems with high dimensionality and many local minima are investigated
under five different optimization algorithms: DIRECT, simulated annealing, Spallâs SPSA algorithm, the KNITRO
package, and QNSTOP, a new algorithm developed at Indiana University
Expanded Combinatorial Designs as Tool to Model Network Slicing in 5G
The network slice management function (NSMF) in 5G has a task to configure
the network slice instances and to combine network slice subnet instances from
the new-generation radio access network and the core network into an end-to-end
network slice instance. In this paper, we propose a mathematical model for
network slicing based on combinatorial designs such as Latin squares and
rectangles and their conjugate forms. We extend those designs with attributes
that offer different levels of abstraction. For one set of attributes we prove
a stability Lemma for the necessary conditions to reach a stationary ergodic
stage. We also introduce a definition of utilization ratio function and offer
an algorithm for its maximization. Moreover, we provide algorithms that
simulate the work of NSMF with randomized or optimized strategies, and we
report the results of our implementation, experiments and simulations for one
set of attributes.Comment: Accepted for publication in IEEE Acces
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