Histogram Rule: Oracle Inequality and Learning Rates

2020 ◽  
pp. 35-57
Author(s):  
Ingrid Karin Blaschzyk
Keyword(s):  
Oracle Inequality ◽  
Learning Rates

Infant-Directed Speech Effects in Adult Artificial Grammar Learning

2019 ◽  
Author(s):  
Krisztina Sára Lukics ◽  
Ágnes Lukács
Keyword(s):  
Language Acquisition ◽  
Natural Language ◽  
First Language ◽  
Higher Learning ◽  
Artificial Grammar ◽  
Combined Effect ◽  
First Language Acquisition ◽  
Relative Contribution ◽  
Grammar Learning ◽  
Learning Rates

First language acquisition is facilitated by several characteristics of infant-directed speech, but we know little about their relative contribution to learning different aspects of language. We investigated infant-directed speech effects on the acquisition of a linear artificial grammar in two experiments. We examined the effect of incremental presentation of strings (starting small) and prosody (comparing monotonous, arbitrary and phrase prosody). Presenting shorter strings before longer ones led to higher learning rates compared to random presentation. Prosody marking phrases had a similar effect, yet, prosody without marking syntactic units did not facilitate learning. These studies were the first to test the starting small effect with a linear artificial grammar, and also the first to investigate the combined effect of starting small and prosody. Our results suggest that starting small and prosody facilitate the extraction of regularities from artificial linguistic stimuli, indicating they may play an important role in natural language acquisition.

scholarly journals Author Correction: The effect of training intensity on implicit learning rates in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Natasza D. Orlov ◽  
Jessica Sanderson ◽  
Syed Ali Muqtadir ◽  
Anastasia K. Kalpakidou ◽  
Panayiota G. Michalopoulou ◽  
...  
Keyword(s):  
Implicit Learning ◽  
Training Intensity ◽  
Learning Rates

scholarly journals Electromagnetic Actuator System Using Witty Control System

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 65
Author(s):  
Der-Fa Chen ◽  
Shen-Pao-Chi Chiu ◽  
An-Bang Cheng ◽  
Jung-Chu Ting
Keyword(s):  
Control System ◽  
Control Method ◽  
Search Algorithm ◽  
Electromagnetic Actuator ◽  
Control Performance ◽  
Learning Capability ◽  
Learning Rates ◽  
Agricultural Applications ◽  
Actuator System ◽  
Rice Milling

Electromagnetic actuator systems composed of an induction servo motor (ISM) drive system and a rice milling machine system have widely been used in agricultural applications. In order to achieve a finer control performance, a witty control system using a revised recurrent Jacobi polynomial neural network (RRJPNN) control and two remunerated controls with an altered bat search algorithm (ABSA) method is proposed to control electromagnetic actuator systems. The witty control system with finer learning capability can fulfill the RRJPNN control, which involves an attunement law, two remunerated controls, which have two evaluation laws, and a dominator control. Based on the Lyapunov stability principle, the attunement law in the RRJPNN control and two evaluation laws in the two remunerated controls are derived. Moreover, the ABSA method can acquire the adjustable learning rates to quicken convergence of weights. Finally, the proposed control method exhibits a finer control performance that is confirmed by experimental results.

INDEFINITE KERNEL NETWORK WITH DEPENDENT SAMPLING

2013 ◽  
Vol 11 (05) ◽  
pp. 1350020 ◽  
Author(s):  
HONGWEI SUN ◽  
QIANG WU
Keyword(s):  
Hilbert Spaces ◽  
Reproducing Kernel ◽  
Function Class ◽  
Positive Definiteness ◽  
Reproducing Kernel Hilbert Spaces ◽  
Strong Mixing ◽  
Mixing Condition ◽  
Indefinite Kernel ◽  
Learning Rates ◽  
Kernel Hilbert Spaces

We study the asymptotical properties of indefinite kernel network with coefficient regularization and dependent sampling. The framework under investigation is different from classical kernel learning. Positive definiteness is not required by the kernel function and the samples are allowed to be weakly dependent with the dependence measured by a strong mixing condition. By a new kernel decomposition technique introduced in [27], two reproducing kernel Hilbert spaces and their associated kernel integral operators are used to characterize the properties and learnability of the hypothesis function class. Capacity independent error bounds and learning rates are deduced.

scholarly journals Globally convergent algorithms with local learning rates

2002 ◽  
Vol 13 (3) ◽  
pp. 774-779 ◽  
Author(s):  
G.D. Magoulas ◽  
V.P. Plagianakos ◽  
M.N. Vrahatis
Keyword(s):  
Local Learning ◽  
Globally Convergent ◽  
Learning Rates

scholarly journals Fluid Genetic Algorithm (FGA)

2017 ◽  
Vol 4 (2) ◽  
pp. 158-167 ◽  
Author(s):  
Ruholla Jafari-Marandi ◽  
Brian K. Smith
Keyword(s):  
Genetic Algorithm ◽  
Success Rate ◽  
Optimization Problems ◽  
Engineering Problem ◽  
Review Of The Literature ◽  
Learning Rates ◽  
New Concepts ◽  
Convergence Control ◽  
Multi Level ◽  
Paper Method

Abstract Genetic Algorithm (GA) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA), some of these concepts are changed, removed, and furthermore, new concepts are introduced. The performance of GA and FGA are compared through seven benchmark functions. FGA not only shows a better success rate and better convergence control, but it can be applied to a wider range of problems including multi-objective and multi-level problems. Also, the application of FGA for a real engineering problem, Quadric Assignment Problem (AQP), is shown and experienced. Highlights This work presents a novel Genetic Algorithm alteration. Chromosome concept and structure in FGA is more similar to the real genetic world. FGA comprises global and individual learning rates. We show FGA enjoys higher success rate, and better convergence control.

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