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Journal articles on the topic 'Sequential training'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Colombetti, Marco, and Marco Dorigo. "Training Agents to Perform Sequential Behavior." Adaptive Behavior 2, no. 3 (1994): 247–75. http://dx.doi.org/10.1177/105971239400200302.

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2

Lewis, David D. "A sequential algorithm for training text classifiers." ACM SIGIR Forum 29, no. 2 (1995): 13–19. http://dx.doi.org/10.1145/219587.219592.

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3

Thompson, D. E., and Sunggyu Kwon. "Neighborhood sequential and random training techniques for CMAC." IEEE Transactions on Neural Networks 6, no. 1 (1995): 196–202. http://dx.doi.org/10.1109/72.363437.

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4

Frances-Villora, Jose V., Alfredo Rosado-Muñoz, Manuel Bataller-Mompean, Juan Barrios-Aviles, and Juan F. Guerrero-Martinez. "Moving Learning Machine towards Fast Real-Time Applications: A High-Speed FPGA-Based Implementation of the OS-ELM Training Algorithm." Electronics 7, no. 11 (2018): 308. http://dx.doi.org/10.3390/electronics7110308.

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Currently, there are some emerging online learning applications handling data streams in real-time. The On-line Sequential Extreme Learning Machine (OS-ELM) has been successfully used in real-time condition prediction applications because of its good generalization performance at an extreme learning speed, but the number of trainings by a second (training frequency) achieved in these continuous learning applications has to be further reduced. This paper proposes a performance-optimized implementation of the OS-ELM training algorithm when it is applied to real-time applications. In this case, t

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Xu, Youjiang, Yahong Han, Richang Hong, and Qi Tian. "Sequential Video VLAD: Training the Aggregation Locally and Temporally." IEEE Transactions on Image Processing 27, no. 10 (2018): 4933–44. http://dx.doi.org/10.1109/tip.2018.2846664.

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6

Du, Xu, John Tadrous, and Ashutosh Sabharwal. "Sequential Beamforming for Multiuser MIMO With Full-Duplex Training." IEEE Transactions on Wireless Communications 15, no. 12 (2016): 8551–64. http://dx.doi.org/10.1109/twc.2016.2616338.

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7

Ñanculef, Ricardo, Carlos Valle, Héctor Allende, and Claudio Moraga. "Training regression ensembles by sequential target correction and resampling." Information Sciences 195 (July 2012): 154–74. http://dx.doi.org/10.1016/j.ins.2012.01.035.

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8

Dai, Di, Huaxiong Li, Xiuyi Jia, Xianzhong Zhou, Bing Huang, and Sunning Liang. "A co-training approach for sequential three-way decisions." International Journal of Machine Learning and Cybernetics 11, no. 5 (2020): 1129–39. http://dx.doi.org/10.1007/s13042-020-01086-7.

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Tsang, William W. N., Kelly L. Gao, K. M. Chan, Sheila Purves, Duncan J. Macfarlane, and Shirley S. M. Fong. "Sitting Tai Chi Improves the Balance Control and Muscle Strength of Community-Dwelling Persons with Spinal Cord Injuries: A Pilot Study." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/523852.

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Objective. To investigate the effects of sitting Tai Chi on muscle strength, balance control, and quality of life (QOL) among survivors with spinal cord injuries (SCI).Methods. Eleven SCI survivors participated in the sitting Tai Chi training (90 minutes/session, 2 times/week for 12 weeks) and eight SCI survivors acted as controls. Dynamic sitting balance was evaluated using limits of stability test and a sequential weight shifting test in sitting. Handgrip strength was also tested using a hand-held dynamometer. QOL was measured using the World Health Organization’s Quality of Life Scale.Resul

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Vinterbo, S., and L. Ohno-Machado. "Effects of Case Removal in Prognostic Models." Methods of Information in Medicine 40, no. 01 (2001): 32–38. http://dx.doi.org/10.1055/s-0038-1634461.

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AbstractConstructing and updating prognostic models that learn from training cases is a time-consuming task. The more compact, and yet informative, the training sets are, the faster one can build and properly evaluate such models. We have compared different regression diagnostic methods for selection and removal of training cases in prognostic models. Univariate determinations were performed using classical regression diagnostic statistics. Multivariate determinations were performed using (1) a sequential “backward” selection of cases, and (2) a non-sequential genetic algorithm. The genetic al

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Olson, Jacilyn M., and Linda Sealey Holtz. "Combined and Sequential Physical and Vocal Training for Parkinson’s Disease." Medicine & Science in Sports & Exercise 48 (May 2016): 715. http://dx.doi.org/10.1249/01.mss.0000487146.37601.43.

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12

Akhand, M. A. H., Md Monirul Islam, and K. Murase. "Progressive interactive training: A sequential neural network ensemble learning method." Neurocomputing 73, no. 1-3 (2009): 260–73. http://dx.doi.org/10.1016/j.neucom.2009.09.001.

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13

WANG, DI, and NARENDRA S. CHAUDHARI. "BINARY NEURAL NETWORK TRAINING ALGORITHMS BASED ON LINEAR SEQUENTIAL LEARNING." International Journal of Neural Systems 13, no. 05 (2003): 333–51. http://dx.doi.org/10.1142/s0129065703001613.

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A key problem in Binary Neural Network learning is to decide bigger linear separable subsets. In this paper we prove some lemmas about linear separability. Based on these lemmas, we propose Multi-Core Learning (MCL) and Multi-Core Expand-and-Truncate Learning (MCETL) algorithms to construct Binary Neural Networks. We conclude that MCL and MCETL simplify the equations to compute weights and thresholds, and they result in the construction of simpler hidden layer. Examples are given to demonstrate these conclusions.

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14

Cancelliere, Rossella, and Roberto Gemello. "Efficient training of Time Delay Neural Networks for sequential patterns." Neurocomputing 10, no. 1 (1996): 33–42. http://dx.doi.org/10.1016/0925-2312(95)00044-5.

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15

Pangborn, Meghan M., Carrie S. W. Borrero, and John C. Borrero. "SEQUENTIAL APPLICATION OF CAREGIVER TRAINING TO IMPLEMENT PEDIATRIC FEEDING PROTOCOLS." Behavioral Interventions 28, no. 2 (2012): 107–30. http://dx.doi.org/10.1002/bin.1356.

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16

Daydar, Akshay. "Development of Effective Artificial Neural Network Model using Sequential Sensitivity Analysis and Randomized Training." International Journal of Soft Computing and Engineering 10, no. 6 (2021): 12–20. http://dx.doi.org/10.35940/ijsce.f3515.0710621.

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As the machine learning algorithms evolve, there is a growing need of how to train the algorithm effectively for the large data with available resources in practically less time. The paper presents an idea of developing an effective model that focuses on the implementation of sequential sensitivity analysis and randomized training approach which can be one solution to this growing need. Many researchers focused on the implementation of sensitivity analysis to eliminate the insignificant features ands reduce the complexity in data selection. These sensitivity analysis methods relatively take a

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17

Tian, Li Yan, and Xiao Guang Hu. "Method of Parallel Sequential Minimal Optimization for Fast Training Support Vector Machine." Applied Mechanics and Materials 29-32 (August 2010): 947–51. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.947.

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A fast training support vector machine using parallel sequential minimal optimization is presented in this paper. Up to now, sequential minimal optimization (SMO) is one of the major algorithms for training SVM, but it still requires a large amount of computation time for the large sample problems. Unlike the traditional SMO, the parallel SMO partitions the entire training data set into small subsets first and then runs multiple CPU processors to seal with each of the partitioned data set. Experiments show that the new algorithm has great advantage in terms of speediness when applied to proble

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18

Fisher, James P., and Robert Csapo. "Periodization and Programming in Sports." Sports 9, no. 2 (2021): 13. http://dx.doi.org/10.3390/sports9020013.

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19

Travis, S. Kyle, Ai Ishida, Christopher B. Taber, Andrew C. Fry, and Michael H. Stone. "Emphasizing Task-Specific Hypertrophy to Enhance Sequential Strength and Power Performance." Journal of Functional Morphology and Kinesiology 5, no. 4 (2020): 76. http://dx.doi.org/10.3390/jfmk5040076.

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While strength is indeed a skill, most discussions have primarily considered structural adaptations rather than ultrastructural augmentation to improve performance. Altering the structural component of the muscle is often the aim of hypertrophic training, yet not all hypertrophy is equal; such alterations are dependent upon how the muscle adapts to the training stimuli and overall training stress. When comparing bodybuilders to strength and power athletes such as powerlifters, weightlifters, and throwers, while muscle size may be similar, the ability to produce force and power is often inequiv

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20

Weir, Michael K., and Li Hui Chen. "Extending Learning Feasibility Through Feedforward Sequential Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 6 (1998): 228–33. http://dx.doi.org/10.20965/jaciii.1998.p0228.

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In this paper, a sequence-based neural network approach called feedforward sequential learning (FSL) is proposed for extending the range of feasibility for feedforward networks in the three areas of architecture, training, and generalization. The extension is enabled through a spatio-temporal indexing scheme that decomposes the task into a sequence of simpler subproblems. Each subproblem is then solved by a separate weight state. The separate trained weight states are then combined into a continuous final weight state sequence to enable smooth generalization. FSL can be used to train mappings

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21

Erickson, Michael J., Brian A. Colle, and Joseph J. Charney. "Impact of Bias-Correction Type and Conditional Training on Bayesian Model Averaging over the Northeast United States." Weather and Forecasting 27, no. 6 (2012): 1449–69. http://dx.doi.org/10.1175/waf-d-11-00149.1.

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Abstract The performance of a multimodel ensemble over the northeast United States is evaluated before and after applying bias correction and Bayesian model averaging (BMA). The 13-member Stony Brook University (SBU) ensemble at 0000 UTC is combined with the 21-member National Centers for Environmental Prediction (NCEP) Short-Range Ensemble Forecast (SREF) system at 2100 UTC. The ensemble is verified using 2-m temperature and 10-m wind speed for the 2007–09 warm seasons, and for subsets of days with high ozone and high fire threat. The impacts of training period, bias-correction method, and BM

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22

Sakurai, Shigeaki, Kyoko Makino, and Shigeru Matsumoto. "An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/871412.

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This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount

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23

Lee, Changki, and Myung-Gil Jang. "Fast Training of Structured SVM Using Fixed-Threshold Sequential Minimal Optimization." ETRI Journal 31, no. 2 (2009): 121–28. http://dx.doi.org/10.4218/etrij.09.0108.0276.

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24

Guo, Y., X. Jia, and D. Paull. "SEQUENTIAL CLASSIFIER TRAINING FOR RICE MAPPING WITH MULTITEMPORAL REMOTE SENSING IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 20, 2017): 161–65. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-161-2017.

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Most traditional methods for rice mapping with remote sensing data are effective when they are applied to the initial growing stage of rice, as the practice of flooding during this period makes the spectral characteristics of rice fields more distinguishable. In this study, we propose a sequential classifier training approach for rice mapping that can be used over the whole growing period of rice for monitoring various growth stages. Rice fields are firstly identified during the initial flooding period. The identified rice fields are used as training data to train a classifier that separates r

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25

Stobiecki, Paweł, and Bartłomiej Śnieżyński. "Training Example Generation Method for Supervised Learning Agents in Sequential Scenarios." Procedia Computer Science 35 (2014): 44–53. http://dx.doi.org/10.1016/j.procs.2014.08.083.

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26

Vercauteren, Tom, Pradeep Aggarwal, Xiaodong Wang, and Ta-Hsin Li. "Hierarchical Forecasting of Web Server Workload Using Sequential Monte Carlo Training." IEEE Transactions on Signal Processing 55, no. 4 (2007): 1286–97. http://dx.doi.org/10.1109/tsp.2006.889401.

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27

Cao, L. J., S. S. Keerthi, C. J. Ong, P. Uvaraj, X. J. Fu, and H. P. Lee. "Developing parallel sequential minimal optimization for fast training support vector machine." Neurocomputing 70, no. 1-3 (2006): 93–104. http://dx.doi.org/10.1016/j.neucom.2006.05.007.

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28

Cao, L. J., S. S. Keerthi, C. J. Ong, et al. "Parallel Sequential Minimal Optimization for the Training of Support Vector Machines." IEEE Transactions on Neural Networks 17, no. 4 (2006): 1039–49. http://dx.doi.org/10.1109/tnn.2006.875989.

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29

Abe, Shigeo. "Fusing sequential minimal optimization and Newton’s method for support vector training." International Journal of Machine Learning and Cybernetics 7, no. 3 (2014): 345–64. http://dx.doi.org/10.1007/s13042-014-0265-x.

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30

Beardsley, Mark M., Stanford E. Rubin, and William E. Garner. "A Systematic Model for Designing and Implementing Staff Development and Training Programs for Rehabilitation Counselors." Journal of Applied Rehabilitation Counseling 18, no. 2 (1987): 11–14. http://dx.doi.org/10.1891/0047-2220.18.2.11.

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A sequential model for designing and implementing staff training programs for rehabilitation counselors (RCs) is presented. The model includes six steps or phases and emphasizes identification of RC job tasks, establishment of specific training goals, and evaluation of training outcomes. Applications of the model are discussed.

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31

Freitas, J. F. G. de, M. Niranjan, A. H. Gee, and A. Doucet. "Sequential Monte Carlo Methods to Train Neural Network Models." Neural Computation 12, no. 4 (2000): 955–93. http://dx.doi.org/10.1162/089976600300015664.

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We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving on-line, nonlinear, and nongaussian signal proces

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Jang, Ilsik, Seeun Oh, Yumi Kim, Changhyup Park, and Hyunjeong Kang. "Well-placement optimisation using sequential artificial neural networks." Energy Exploration & Exploitation 36, no. 3 (2017): 433–49. http://dx.doi.org/10.1177/0144598717729490.

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In this study, a new algorithm is proposed by employing artificial neural networks in a sequential manner, termed the sequential artificial neural network, to obtain a global solution for optimizing the drilling location of oil or gas reservoirs. The developed sequential artificial neural network is used to successively narrow the search space to efficiently obtain the global solution. When training each artificial neural network, pre-defined amount of data within the new search space are added to the training dataset to improve the estimation performance. When the size of the search space mee

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Blanchard, Gilles. "Different Paradigms for Choosing Sequential Reweighting Algorithms." Neural Computation 16, no. 4 (2004): 811–36. http://dx.doi.org/10.1162/089976604322860712.

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Analyses of the success of ensemble methods in classification have pointed out the important role played by the margin distribution function on the training and test sets. While it is acknowledged that one should generally try to achieve high margins on the training set, the more precise shape of the empirical margin distribution function one should favor in practice is subject to different approaches. We first present two concurrent philosophies for choosing the empirical margin profile: the minimax margin paradigm and the mean and variance paradigm. The best-known representative of the first

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34

Sha, Yang, You Jian-Ping, Zhang Hui-Lan, and Luo Hong-Xia. "Study on the Effectiveness Evaluation of Personal Protective Equipment for Health Care Staff Trained with Graphical Interpretation and Operation." Prehospital and Disaster Medicine 34, s1 (2019): s168—s169. http://dx.doi.org/10.1017/s1049023x19003856.

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Introduction:Proper use of personal protective equipment (PPE) is essential when facing emerging infectious diseases. Proper training methods can promote the use of the PPE correctly.Aim:To explore the effect of the training method of sequential operation training on medical staff to master PPE penetration and removal skills, and to study the memory attenuation after training.Methods:Fifteen medical staff with no experience of PPE operation in a hospital were trained to wear PPE in accordance with WHO standards by illustration and sequential operation method. The training included 30 minutes o

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35

Arslan, Hasan Sait, Kairit Sirts, Mark Fishel, and Gholamreza Anbarjafari. "Multimodal Sequential Fashion Attribute Prediction." Information 10, no. 10 (2019): 308. http://dx.doi.org/10.3390/info10100308.

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We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, t

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36

Ensour, Waed, Dia Zeglat, and Fayiz Shrafat. "Impact of job satisfaction on training motivation." Problems and Perspectives in Management 16, no. 3 (2018): 337–55. http://dx.doi.org/10.21511/ppm.16(3).2018.27.

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This study aims to introduce new insights regarding factors influencing employees’ level of training motivation through investigating the impact of job satisfaction dimensions (pay, fringe benefits, contingent rewards, promotion, supervision, co-workers, operating conditions, nature of work and communication) on training motivation.Data were collected from 342 participants from six Jordanian ministries. The multiple regression technique was utilized to examine the predictive power of job satisfaction dimensions on training motivation. At the next stage, three sequential MR analysis rounds were

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37

McLaughlin, Anne C., Wendy A. Rogers, and Arthur D. Fisk. "Effectiveness of Audio and Visual Training Presentation Modes for Glucometer Calibration." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 25 (2002): 2059–63. http://dx.doi.org/10.1177/154193120204602514.

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This experiment investigated whether different presentation modes of instructional materials are differentially effective for older and younger adults learning to calibrate a glucometer. Glucometers are complex and require serial, sequential steps to calibrate them successfully. Some previous studies have failed to find a difference for older adults between instructions presented via audio and instructions presented with both audio and video (e.g. Stine, Wingfield & Myers, 1990); others have even found audio superior to video for presentation of instructions (Meline, 1976; Hale, 1998). In

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38

Sahoo, Sujit Kumar, and Anamitra Makur. "Sparse Sequential Generalization of K-means for dictionary training on noisy signals." Signal Processing 129 (December 2016): 62–66. http://dx.doi.org/10.1016/j.sigpro.2016.05.036.

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39

Selmer, Jan, Ingemar Torbiorn, and Corinna T. de Leon. "Sequential cross-cultural training for expatriate business managers: predeparture and post-arrival." International Journal of Human Resource Management 9, no. 5 (1998): 831–40. http://dx.doi.org/10.1080/095851998340829.

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40

Threatt, Tiffaney, Eileen Ward, Kayce Shealy, Amy Hynes, and P. Elizabeth Robinette. "Student pharmacist perceived benefit of a sequential training model for health testing." Currents in Pharmacy Teaching and Learning 9, no. 2 (2017): 324–31. http://dx.doi.org/10.1016/j.cptl.2016.11.015.

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41

Severtson, Jamie M., and James E. Carr. "Training Novice Instructors to Implement Errorless Discrete-Trial Teaching: A Sequential Analysis." Behavior Analysis in Practice 5, no. 2 (2012): 13–23. http://dx.doi.org/10.1007/bf03391820.

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42

Makarov, Vadim V. "The neural network to identify an object by a sequential training mode." Procedia Computer Science 190 (2021): 532–39. http://dx.doi.org/10.1016/j.procs.2021.06.062.

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43

Bilal, Ahmad Raza, and Tehreem Fatima. "Being Alert Matters! A Sequential Interplay of Entrepreneurial Training and Business Outcomes." Academy of Management Proceedings 2021, no. 1 (2021): 16030. http://dx.doi.org/10.5465/ambpp.2021.16030abstract.

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Zhou, Zhiyu, Hanxuan Guo, Yaming Wang, Zefei Zhu, Jiang Wu, and Xiangqi Liu. "Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm." International Journal of Advanced Robotic Systems 15, no. 4 (2018): 172988141879299. http://dx.doi.org/10.1177/1729881418792992.

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This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initialize

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45

Sanderson, Penelope M., and Peter J. Benda. "Exploring Sequential Data: Commentary on Bowers, Jentsch, Salas, and Braun (1998)." Human Factors: The Journal of the Human Factors and Ergonomics Society 40, no. 4 (1998): 680–84. http://dx.doi.org/10.1518/001872098779649328.

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In an investigation intended to determine training needs of flight crews, Bowers et al. (1998, this issue) report two studies showing that the patterning of communication is a better discriminator of good and poor crews than is the content of communication. Bowers et al. characterize their studies as intended to generate hypotheses for training needs and draw connections with Exploratory Sequential Data Analysis (ESDA). Although applauding the intentions of Bowers et al., we point out some concerns with their characterization and implementation of ESDA. Our principal concern is that the Bowers

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Ferry, Brian, John Hedberg, and Barry Harper. "Strategies of Teachers as Users of Interactive Multimedia." Australian Journal of Education 40, no. 2 (1996): 133–46. http://dx.doi.org/10.1177/000494419604000202.

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This study reports the results of an evaluation study of a training process that provided teachers with information about how to use an interactive multimedia package for classroom instruction The ways in which the teachers accessed relevant printed text and hypertext information were mapped and analysed. Search patterns for each information source were tentatively classified into two cognitive strategies labelled as ‘sequential search strategies’ and ‘goal-oriented search strategies’. It was found that the ‘sequential search strategies’ broke down in the non-sequential hypertext environment a

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47

Bondarenko, O., O. Gudovych, O. Kovaliov, and V. Mazurenko. "METHODICAL ASPECTS OF BUSINESS GAME ON THE MANAGEMENT AUTHORITY ACTIONS IN EMERGENCY SITUATIONS." Collected Scientific Papers of the Institute of Public Administration in the Sphere of Civil Protection 7 (December 22, 2019): 5–16. http://dx.doi.org/10.35577/iducz.2019.07.01.

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Successful implementation of civil protection measure depends on the level of training of executive bodies, staffs, services, command and staff of the civil protection governing bodies. Conducting exercises and trainings with a certain frequency allows to work out the whole set of events connected with the civil protection reality plans check, preparing the executive bodies for the intended purpose, organizing and conducting rescue and other emergency operations in difficult emergency situations within the short period of time. According to the experience obtained, one of the response em

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Kumar, S., M. Anand Kumar, and K. P. Soman. "Deep Learning Based Part-of-Speech Tagging for Malayalam Twitter Data (Special Issue: Deep Learning Techniques for Natural Language Processing)." Journal of Intelligent Systems 28, no. 3 (2019): 423–35. http://dx.doi.org/10.1515/jisys-2017-0520.

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Abstract The paper addresses the problem of part-of-speech (POS) tagging for Malayalam tweets. The conversational style of posts/tweets/text in social media data poses a challenge in using general POS tagset for tagging the text. For the current work, a tagset was designed that contains 17 coarse tags and 9915 tweets were tagged manually for experiment and evaluation. The tagged data were evaluated using sequential deep learning methods like recurrent neural network (RNN), gated recurrent units (GRU), long short-term memory (LSTM), and bidirectional LSTM (BLSTM). The training of the model was

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Zhao, Baigan, Yingping Huang, Hongjian Wei, and Xing Hu. "Ego-Motion Estimation Using Recurrent Convolutional Neural Networks through Optical Flow Learning." Electronics 10, no. 3 (2021): 222. http://dx.doi.org/10.3390/electronics10030222.

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Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in

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Buzzi, C., L. Grippo, and M. Sciandrone. "Convergent Decomposition Techniques for Training RBF Neural Networks." Neural Computation 13, no. 8 (2001): 1891–920. http://dx.doi.org/10.1162/08997660152469396.

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In this article we define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers. Then we define a decomposition algorithm in which the selection of the center locations is split into sequential minimizations with respect to each center, and we give a suitable criterion for choosing the centers that must be updated at each step. We prove the global convergence of the propose

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