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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Supervised Motor Learning"

1

Xian, Xiaoyu, Haichuan Tang, Yin Tian, Qi Liu, and Yuming Fan. "Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors." Journal of Physics: Conference Series 2095, no. 1 (November 1, 2021): 012058. http://dx.doi.org/10.1088/1742-6596/2095/1/012058.

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Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.
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2

Et. al., Rameshwar D. Chintamani,. "Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 26, 2021): 541–53. http://dx.doi.org/10.17762/itii.v9i2.381.

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The brain-computer interface provides the excellent potential to address nervous system-related activity. The function of the nervous system work between internal brain control and external human body physical structure. Some parts of the human body cannot generate the signal for the processing of the human brain, cannot recognize and identify human body parts' activity—the motor imagery EEG classification approach helps resolve such types of critical illness cause of death. The dimension and structure of motor imagery-based EEG data are very high and unsupported behaviors. The machine learning and another classification algorithm cannot handle these variants of EEG data directly. For the process of better classification of motor imagery, EEG needs transformation and extraction. The transform-based feature extraction process such as DCT, DWT, SFTF and some other harmonic frequency-based applied. In this paper presents the details analysis of feature extraction and classification algorithms for motor imagery EEG classification. The machine learning provides three types of an algorithm for classification, supervised, unsupervised and semi-supervised. This paper mainly focuses on the supervised machine learning algorithm. For the analysis of machine learning algorithm use BC competition-IV dataset. MATLAB software is used as a tool for the code of algorithms and measures standard parameters such as accuracy, sensitivity and specificity.
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3

Singh, Puneet, Sumitash Jana, Ashitava Ghosal, and Aditya Murthy. "Exploration of joint redundancy but not task space variability facilitates supervised motor learning." Proceedings of the National Academy of Sciences 113, no. 50 (November 29, 2016): 14414–19. http://dx.doi.org/10.1073/pnas.1613383113.

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The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.
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4

She, Zhou, Gan, Ma, and Luo. "Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning." Electronics 8, no. 11 (November 1, 2019): 1273. http://dx.doi.org/10.3390/electronics8111273.

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In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals.
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5

Raymond, Jennifer L., and Javier F. Medina. "Computational Principles of Supervised Learning in the Cerebellum." Annual Review of Neuroscience 41, no. 1 (July 8, 2018): 233–53. http://dx.doi.org/10.1146/annurev-neuro-080317-061948.

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Анотація:
Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.
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6

Jigyasu, R., V. Shrivastava, and S. Singh. "Prognostics and health management of induction motor by supervised learning classifiers." IOP Conference Series: Materials Science and Engineering 1168, no. 1 (July 1, 2021): 012006. http://dx.doi.org/10.1088/1757-899x/1168/1/012006.

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7

Zhang, Weiwei, Deji Chen, and Yang Kong. "Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images." Sensors 21, no. 14 (July 13, 2021): 4774. http://dx.doi.org/10.3390/s21144774.

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The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.
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8

Tang, Xian-Lun, Wei-Chang Ma, De-Song Kong, and Wei Li. "Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition." Neural Computation 31, no. 5 (May 2019): 919–42. http://dx.doi.org/10.1162/neco_a_01183.

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Анотація:
Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence. Prior knowledge is introduced into the intermediary layer of the deep stacking network, and a restricted Boltzmann machine is trained by a semisupervised method in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Several EEG data sets are carried out to evaluate the performance of the proposed method. The results show that the recognition accuracy of SADSN is advanced with a more significant convergence rate and successfully classifies motor imagery.
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9

Pyle, Ryan, and Robert Rosenbaum. "A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity." Neural Computation 31, no. 7 (July 2019): 1430–61. http://dx.doi.org/10.1162/neco_a_01198.

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Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopamine-modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models.
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10

Cingireddy, Anirudh Reddy, Robin Ghosh, Venkata Kiran Melapu, Sravanthi Joginipelli, and Tor A. Kwembe. "Classification of Parkinson's Disease Using Motor and Non-Motor Biomarkers Through Machine Learning Techniques." International Journal of Quantitative Structure-Property Relationships 7, no. 2 (April 2022): 1–21. http://dx.doi.org/10.4018/ijqspr.290011.

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Анотація:
Parkinson's disease (PD) is the second most neurodegenerative disease in the United States of America after Alzheimer's disease. The Parkinson's disease patients and scans without evidence for dopaminergic deficit (SWEDD) patients will share the same symptoms, and It's hard to differentiate the PD, SWEDD patients, and healthy controls in the progression of PD. In this research, we classified PD patients, SWEDD patients, and healthy controls by considering motor and non-motor biomarkers, namely MDS-UPDRS part 1, SCOPA score, and QUIP-RS from the PPMI database by using supervised and unsupervised machine learning algorithms, namely Knn, logistic regression, XGBooting, naive Bayes, Decision tree, Random Forest, Support vector machine, multilayer perceptron , and K-means clustering, respectively. Random Forest scored 0.98 percent accuracy among all these algorithms and can identify and differentiate PD, SWEDD, and Healthy controls patients by motor and non-motor biomarkers.
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Дисертації з теми "Supervised Motor Learning"

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Coen, Michael Harlan. "Multimodal dynamics : self-supervised learning in perceptual and motor systems." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34022.

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Анотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 178-192).
This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century demonstrating the extraordinary degree of interaction between sensory modalities during the course of ordinary perception. We develop a framework for creating artificial perceptual systems that draws on these findings, where the primary architectural motif is the cross-modal transmission of perceptual information to enhance each sensory channel individually. We present self-supervised algorithms for learning perceptual grounding, intersensory influence, and sensorymotor coordination, which derive training signals from internal cross-modal correlations rather than from external supervision. Our goal is to create systems that develop by interacting with the world around them, inspired by development in animals. We demonstrate this framework with: (1) a system that learns the number and structure of vowels in American English by simultaneously watching and listening to someone speak. The system then cross-modally clusters the correlated auditory and visual data.
(cont.) It has no advance linguistic knowledge and receives no information outside of its sensory channels. This work is the first unsupervised acquisition of phonetic structure of which we are aware, outside of that done by human infants. (2) a system that learns to sing like a zebra finch, following the developmental stages of a juvenile zebra finch. It first learns the song of an adult male and then listens to its own initially nascent attempts at mimicry through an articulatory synthesizer. In acquiring the birdsong to which it was initially exposed, this system demonstrates self-supervised sensorimotor learning. It also demonstrates afferent and efferent equivalence - the system learns motor maps with the same computational framework used for learning sensory maps.
by Michael Harlan Coen.
Ph.D.
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2

De, La Bourdonnaye François. "Learning sensori-motor mappings using little knowledge : application to manipulation robotics." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC037/document.

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La thèse consiste en l'apprentissage d'une tâche complexe de robotique de manipulation en utilisant très peu d'aprioris. Plus précisément, la tâche apprise consiste à atteindre un objet avec un robot série. L'objectif est de réaliser cet apprentissage sans paramètres de calibrage des caméras, modèles géométriques directs, descripteurs faits à la main ou des démonstrations d'expert. L'apprentissage par renforcement profond est une classe d'algorithmes particulièrement intéressante dans cette optique. En effet, l'apprentissage par renforcement permet d’apprendre une compétence sensori-motrice en se passant de modèles dynamiques. Par ailleurs, l'apprentissage profond permet de se passer de descripteurs faits à la main pour la représentation d'état. Cependant, spécifier les objectifs sans supervision humaine est un défi important. Certaines solutions consistent à utiliser des signaux de récompense informatifs ou des démonstrations d'experts pour guider le robot vers les solutions. D'autres consistent à décomposer l'apprentissage. Par exemple, l'apprentissage "petit à petit" ou "du simple au compliqué" peut être utilisé. Cependant, cette stratégie nécessite la connaissance de l'objectif en termes d'état. Une autre solution est de décomposer une tâche complexe en plusieurs tâches plus simples. Néanmoins, cela n'implique pas l'absence de supervision pour les sous tâches mentionnées. D'autres approches utilisant plusieurs robots en parallèle peuvent également être utilisés mais nécessite du matériel coûteux. Pour notre approche, nous nous inspirons du comportement des êtres humains. Ces derniers généralement regardent l'objet avant de le manipuler. Ainsi, nous décomposons la tâche d'atteinte en 3 sous tâches. La première tâche consiste à apprendre à fixer un objet avec un système de deux caméras pour le localiser dans l'espace. Cette tâche est apprise avec de l'apprentissage par renforcement profond et un signal de récompense faiblement supervisé. Pour la tâche suivante, deux compétences sont apprises en parallèle : la fixation d'effecteur et une fonction de coordination main-oeil. Comme la précédente tâche, un algorithme d'apprentissage par renforcement profond est utilisé avec un signal de récompense faiblement supervisé. Le but de cette tâche est d'être capable de localiser l'effecteur du robot à partir des coordonnées articulaires. La dernière tâche utilise les compétences apprises lors des deux précédentes étapes pour apprendre au robot à atteindre un objet. Cet apprentissage utilise les mêmes aprioris que pour les tâches précédentes. En plus de la tâche d'atteinte, un predicteur d'atteignabilité d'objet est appris. La principale contribution de ces travaux est l'apprentissage d'une tâche de robotique complexe en n'utilisant que très peu de supervision
The thesis is focused on learning a complex manipulation robotics task using little knowledge. More precisely, the concerned task consists in reaching an object with a serial arm and the objective is to learn it without camera calibration parameters, forward kinematics, handcrafted features, or expert demonstrations. Deep reinforcement learning algorithms suit well to this objective. Indeed, reinforcement learning allows to learn sensori-motor mappings while dispensing with dynamics. Besides, deep learning allows to dispense with handcrafted features for the state spacerepresentation. However, it is difficult to specify the objectives of the learned task without requiring human supervision. Some solutions imply expert demonstrations or shaping rewards to guiderobots towards its objective. The latter is generally computed using forward kinematics and handcrafted visual modules. Another class of solutions consists in decomposing the complex task. Learning from easy missions can be used, but this requires the knowledge of a goal state. Decomposing the whole complex into simpler sub tasks can also be utilized (hierarchical learning) but does notnecessarily imply a lack of human supervision. Alternate approaches which use several agents in parallel to increase the probability of success can be used but are costly. In our approach,we decompose the whole reaching task into three simpler sub tasks while taking inspiration from the human behavior. Indeed, humans first look at an object before reaching it. The first learned task is an object fixation task which is aimed at localizing the object in the 3D space. This is learned using deep reinforcement learning and a weakly supervised reward function. The second task consists in learning jointly end-effector binocular fixations and a hand-eye coordination function. This is also learned using a similar set-up and is aimed at localizing the end-effector in the 3D space. The third task uses the two prior learned skills to learn to reach an object and uses the same requirements as the two prior tasks: it hardly requires supervision. In addition, without using additional priors, an object reachability predictor is learned in parallel. The main contribution of this thesis is the learning of a complex robotic task with weak supervision
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3

Ahnesjö, Henrik. "Fault detection of planetary gearboxes in BLDC-motors using vibration and acoustic noise analysis." Thesis, Uppsala universitet, Institutionen för elektroteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425966.

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This thesis aims to use vibration and acoustic noise analysis to help a production line of a certain motor type to ensure good quality. Noise from the gearbox is sometimes present and the way it is detected is with a human listening to it. This type of error detection is subjective, and it is possible for human error to be present. Therefore, an automatic test that pass or fail the produced Brush Less Direct Current (BLDC)-motors is wanted. Two measurement setups were used. One was based on an accelerometer which was used for vibration measurements, and the other based on a microphone for acoustic sound measurements. The acquisition and analysis of the measurements were implemented using the data acquisition device, compactDAQ NI 9171, and the graphical programming software, NI LabVIEW. Two methods, i.e., power spectrum analysis and machine learning, were used for the analyzing of vibration and acoustic signals, and identifying faults in the gearbox. The first method based on the Fast Fourier transform (FFT) was used to the recorded sound from the BLDC-motor with the integrated planetary gearbox to identify the peaks of the sound signals. The source of the acoustic sound is from a faulty planet gear, in which a flank of a tooth had an indentation. Which could be measured and analyzed. It sounded like noise, which can be used as the indications of faults in gears. The second method was based on the BLDC-motors vibration characteristics and uses supervised machine learning to separate healthy motors from the faulty ones. Support Vector Machine (SVM) is the suggested machine learning algorithm and 23 different features are used. The best performing model was a Coarse Gaussian SVM, with an overall accuracy of 92.25 % on the validation data.
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4

Singh, Puneet. "The Role of Basal Ganglia and Redundancy in Supervised Motor Learning." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4176.

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Human sensorimotor control can achieve highly reliable movements under circumstances of noise, redundancy, uncertainty, and sensory delays. Our ability to achieve reliable and accurate movements is in the fact we have a nervous system that learns these limitations and continuously compensates for them. The purpose of the thesis is to understand brain mechanisms and computations underlying supervised motor learning, its interaction with reinforcement learning and study its relation to motor variability. To address these issues, we have investigated factors influencing supervised motor learning such as neurological disease condition, the role of the reinforcement signal, motor variability and motor redundancy. Traditionally, supervised or error-based learning and reinforcement or reward based learning are thought to be occurring at anatomically different places and have functionally separate mechanisms. By leveraging the performance of human patients with Parkinson disease and cerebellar ataxia disease, we demonstrate how the presence and absence of dopamine medication and subthalamic deep brain stimulation (STN-DBS) influenced supervised learning. Furthermore, we also show that the presence and absence of reinforcement at the end of the trial profoundly affected learning such that the difference in learning as a consequence of medication reduced significantly. These results suggest that the basal ganglia modulate the gain of supervised learning in the cerebellum based on the reinforcement received at the end of the trial. Furthermore, we explored motor variability (thought to be an unwanted characteristic of the motor system) and investigated its significance and effect on supervised motor learning. We propose that some part of motor variability arises out of the redundancy in the joints in the human arm. We showed that greater uses of redundancy in the arm lead to faster learning across healthy subjects. We observed these both in dynamic perturbation learning and kinematic perturbation learning. Interestingly, we also found differences in the use of redundancy between the dominant hand and non-dominant hand, suggesting that the nervous system actively controls the redundancy. Furthermore, we also observed some directions in reaching are difficult to learn in comparison to others directions. To understand such behavior, we separated direction wise errors and constructed errors ellipses and found out that eccentricity of ellipse change with learning, which suggests brain while reducing errors in learning, is also trying to homogenize the distribution of errors caused by the perturbation. We also found interesting differences between redundancy and motor learning that was selectively impaired in PD patients but not cerebellar patients, possibly pointing to a role of the basal ganglia in processing of the use of redundancy in motor learning. In summary, the results in the thesis provide experimental support for the hypothesis that the basal ganglia modulate the gain of supervised learning and exploration of redundancy aids in learning and that the redundancy component of the motor variability is not noise. In future, we hope that this relationship between basal ganglia, reinforcement, and redundancy in supervised motor learning can be leveraged to enhance motor rehabilitation and motor skills in patients with motor deficits.
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Книги з теми "Supervised Motor Learning"

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Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.

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This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.
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Частини книг з теми "Supervised Motor Learning"

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Wazlawick, Raul Sidnei, and Antonio Carlos da Rocha Costa. "Non-Supervised Sensory-Motor Agents Learning." In Artificial Neural Nets and Genetic Algorithms, 49–52. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_15.

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2

Smrkovsky, Eric, and Hubert Cecotti. "Graph-Based Semi-supervised Learning Using Riemannian Geometry Distance for Motor Imagery Classification." In Lecture Notes in Computer Science, 317–26. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33783-3_30.

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3

Jana, Gopal Chandra, Shivam Shukla, Divyansh Srivastava, and Anupam Agrawal. "Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification." In Intelligent Computing and Applications, 125–41. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5566-4_12.

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4

Premkumar, K., and B. V. Manikandan. "Online Fuzzy Supervised Learning of Radial Basis Function Neural Network Based Speed Controller for Brushless DC Motor." In Lecture Notes in Electrical Engineering, 1397–405. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2119-7_136.

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5

Toscano, David S., and Enrique V. Carrera. "Failure Detection in Induction Motors Using Non-supervised Machine Learning Algorithms." In Systems and Information Sciences, 48–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59194-6_5.

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Kawato, Mitsuo. "Feedback-Error-Learning Neural Network for Supervised Motor Learning." In Advanced Neural Computers, 365–72. Elsevier, 1990. http://dx.doi.org/10.1016/b978-0-444-88400-8.50047-9.

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7

Trappenberg, Thomas P. "Motor Control and Reinforcement Learning." In Fundamentals of Computational Neuroscience, 323–62. 3rd ed. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869364.003.0011.

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Анотація:
Abstract This chapter discusses control and decision systems that learn from general guidance in form of environmental feedback. It starts with a short overview of classical control systems that describe well basic motor control in the brain. The chapter then continues to adaptive control systems that can learn optimal actions from reward feedback. This is somewhat different to the basic supervised learning discussed previously where the teacher would specify precisely the desired response of an agent. It is also different to unsupervised learning as there is still some feedback from the environment in form of reward. The goal of a reinforcement learning agent is to figure out which actions to take in order to achieve optimal returns, often in noisy settings.
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Benarroch, Eduardo E. "Cerebellar Circuits." In Neuroscience for Clinicians, edited by Eduardo E. Benarroch, 610–30. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190948894.003.0033.

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The cerebellum has a critical role in control of timing and coordination of movement, acquisition of skills, and cognitive and affective functions. It participates in motor control via both immediate online adjustments of motor performance and long-term adaptive motor learning, referred to as supervised or error-based learning. Most of the cerebellum is interconnected with association areas of the cerebral cortex. The cerebellum is a major target of genetic, degenerative, metabolic, and immune disorders. Experimental evidence indicates that disrupted Purkinje cell pacemaking activity and synaptic plasticity in the cerebellum have a major role in the pathophysiology of ataxia. The cerebellar circuits also have a major role in the pathophysiology of different types of tremor.
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Arbib, Michael A. "From empathy to mirror neurons and back to aesthetics." In When Brains Meet Buildings, 287–358. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190060954.003.0005.

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This chapter approaches aesthetics anew by considering empathy and Einfühlung, “feeling ourselves into” a work of art or architecture. The key neuroscience is the discovery of mirror neurons in monkeys that inspired the discovery of mirror systems in humans. Unsupervised, supervised, and reinforcement learning, each based on a different rule for synaptic plasticity, are presented as background for a computational model of how mirror neuron wiring is learned. Mirror neurons may serve social interaction, but they also self-monitor in acquiring new behaviors. This is exemplified in modeling how adaptive sequences of behavior may be mastered through learning the desirability and executability of actions. Such opportunistic scheduling complements the role of scripts. Empathy is linked to mirror systems but also depends on systems beyond the mirror. Returning to Einfühlung, we explore how a motor component may enrich our aesthetic appreciation by recognizing the actions and emotions of protagonists in a representational painting, or by gaining some feeling for the actions of the artist, sculptor, or architect in creating the work. Finally, case studies are sampled, including those in neuroaesthetics seeking neural correlates for aesthetic appreciation, that contribute to a tool kit for assessing the experience of buildings to enrich future design.
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Тези доповідей конференцій з теми "Supervised Motor Learning"

1

Zaman, Shafi Md Kawsar, Xiaodong Liang, and Lihong Zhang. "Induction Motor Fault Diagnosis Using Graph-Based Semi-Supervised Learning." In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2020. http://dx.doi.org/10.1109/ccece47787.2020.9255706.

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2

Burfoot, D., and Y. Kuniyoshi. "Semi-supervised learning in a complex arm motor control task." In 2008 IEEE International Conference on Robotics and Biomimetics. IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.4913257.

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3

Han, Jinpei, Xiao Gu, and Benny Lo. "Semi-Supervised Contrastive Learning for Generalizable Motor Imagery EEG Classification." In 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2021. http://dx.doi.org/10.1109/bsn51625.2021.9507038.

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4

Lotey, Taveena, Prateek Keserwani, Gaurav Wasnik, and Partha Pratim Roy. "Cross-Session Motor Imagery EEG Classification using Self-Supervised Contrastive Learning." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956291.

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Rovini, E., A. Moschetti, L. Fiorini, D. Esposito, C. Maremmani, and F. Cavallo. "Wearable Sensors for Prodromal Motor Assessment of Parkinson’s Disease using Supervised Learning*." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856804.

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Dharani, M., and M. Sivachitra. "Motor imagery signal classification using semi supervised and unsupervised extreme learning machines." In 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems. IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8276131.

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7

Bamdadian, A., Cuntai Guan, Kai Keng Ang, and Jianxin Xu. "Online semi-supervised learning with KL distance weighting for Motor Imagery-based BCI." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346529.

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8

Kewalramani, Rohit, and A. Ram. "Estimation of Remaining Useful Life of Electric Motor using supervised deep learning methods." In 2019 IEEE Transportation Electrification Conference (ITEC-India). IEEE, 2019. http://dx.doi.org/10.1109/itec-india48457.2019.itecindia2019-197.

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Papageorgiou, T. D., W. A. Curtis, M. McHenry, and S. M. LaConte. "Neurofeedback of two motor functions using supervised learning-based real-time functional magnetic resonance imaging." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5333703.

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Rajapaksha, Nipuna, Shantha Jayasinghe, Hossein Enshaei, and Nirman Jayarathne. "Supervised Machine Learning Algorithm Selection for Condition Monitoring of Induction Motors." In 2021 IEEE Southern Power Electronics Conference (SPEC). IEEE, 2021. http://dx.doi.org/10.1109/spec52827.2021.9709436.

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