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Analysis of classification algorithms for Machine Learning using the SPSS Method
2023
International Journal of Computer Engineering in Research Trends
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Moreover, machine learning facilitates predictive modeling, enhancing forecasting accuracy for weather, investments, and medical diagnoses, thus aiding in informed decision-making and risk management. ...
Machine learning, a cornerstone of computer science and artificial intelligence, encompasses the use of algorithms to replicate human learning processes and enhance accuracy. ...
Reinforcement Learning: Reinforcement learning is a machine learning paradigm that focuses on training agents to make decisions in an environment. ...
doi:10.22362/ijcert/2023/v10/i07/v10i0703
fatcat:x3ayolz3ljhhpditmm5jflpuva
Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty
[article]
2022
arXiv
pre-print
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In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems. ...
To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed ...
(iii) RTMIC, which is a state-of-the-art medical report generation method based on reinforcement learning, enhancing the capacity of the generation model with reinforcement learning, and increasing the ...
arXiv:2210.13729v1
fatcat:huldbg3mvzeqbg77gik2xc5pou
Variations in Unconditioned Stimulus Processing in Unblocking
2005
Journal of Experimental Psychology Animal Behavior Processes
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Three experiments examined the mechanisms by which downward shifts in reinforcer value influence learning in appetitive unblocking procedures. ...
The results suggested that in normal rats, omission of the second reinforcer enhanced processing of the first reinforcer, rather than processing of the conditioned stimuli, and that lesions of the central ...
Thus, the enhanced learning rate in Group Down-Sham would reflect the upshift in the reinforcer in the test, and the enhanced learning rate in Group Up-Sham would reflect the downshift in the reinforcer ...
doi:10.1037/0097-7403.31.2.155
pmid:15839773
pmcid:PMC1201525
fatcat:ypa2ox2stbdx5nmjziumxgnpha
Reinforced Medical Report Generation with X-Linear Attention and Repetition Penalty
[article]
2020
arXiv
pre-print
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To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where attention mechanisms and reinforcement learning are integrated ...
Therefore, in this work, we propose a reinforced medical report generation solution with x-linear attention and repetition penalty mechanisms (ReMRG-XR) to overcome these problems. ...
, and reinforcement learning is further used to enhance the model's performance. ...
arXiv:2011.07680v1
fatcat:7ttpn7z6jrffpcq2zn657zg66a
Page 9817 of The Journal of Neuroscience Vol. 29, Issue 39
[page]
2008
The Journal of Neuroscience
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In addition, spatial frequency (SF) thresholds generated in reinforcement- based tasks are typically close to sampling limitations set by visual system anatomy, thereby making it difficult to detect experien ...
tant role in the acquirement of normal vision. ...
Feedback modalities as a consideration in biofeedback applications
1978
Behavior Research Methods
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The general issue of concern is when and under what circumstances the amount of feedback (in informational terms) influences the amount of behavior change. ...
The Trowbridge and Cason study showed learning with quantitative feedback to be superior, whereas irrelevant feedback resulted in worse performance than the nofeedback condition. ...
Visual.
Results Learned stabilization. Learned increase and decrease in R-R interval. Increase to success signal; decrease to failure signal. ...
doi:10.3758/bf03205357
fatcat:bywcp6lf65cnpkyehkpshzqvqa
Page 758 of Psychological Abstracts Vol. 62, Issue 3
[page]
1979
Psychological Abstracts
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(d) Create a situation that generates task-specific reinforcement as distinct from task-unrelated reinforcement. (e) Create a situation that can be manipulated to avoid position learning. ...
In each setting contingent reinforcement of comprehension produced comprehension improvements, but there appeared to be little generalization across settings. ...
Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning
[article]
2024
arXiv
pre-print
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Finally, to better reflect the clinical significant and insignificant errors that radiologists would normally assign in the report, we introduce a novel clinical quality reinforcement learning strategy ...
Next, based on the large model's decoder, we develop a multimodal report generator that leverages multimodal prompts from visual features and textual instruction to produce the radiology report in an auto-regressive ...
Additionally, we employ a reinforcement learning mechanism with RadCliQ as the reward function to enhance the clinical accuracy of the generated reports. ...
arXiv:2403.06728v1
fatcat:hgetqpnj6ba3pkcmxcrkwaukfy
Page 727 of Psychological Abstracts Vol. 63, Issue 3
[page]
1980
Psychological Abstracts
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—Normal and language-disabled children were tested for their ability to categorize and generalize from information presented in auditory (verbal) form and visual (nonverbal) forms. The
727 ...
Advantages of the technique included insuring attention, reinforcing the learning of phonemes, guiding the student in difficulties, and providing an indepen- dent support system and enhancing self-esteem ...
Page 1527 of Psychological Abstracts Vol. 42, Issue 10
[page]
1968
Psychological Abstracts
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New Mexico) Tactual-kinesthetic feedback from manipula- tion of visual forms and nondifferential reinforcement in transfer of perceptual learning. ...
Extinction and response com- petition in original and interpolated learning of a visual discrimination. ...
Visual Tracking Using Wang–Landau Reinforcement Sampler
2020
Applied Sciences
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Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. ...
Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang–Landau sampling. ...
We explain reinforcement learning-based visual tracking in Section 3.2 and enhance the proposed visual tracking using Wang-Landau sampling in Section 3.3. ...
doi:10.3390/app10217780
fatcat:b4rmlyhbfnbdjnexh6ekhbuf5e
Improving Generalization with Cross-State Behavior Matching in Deep Reinforcement Learning
2022
International Joint Conference on Autonomous Agents & Multiagent Systems
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Representation learning on visualized input is an essential yet challenging task for deep reinforcement learning (RL). ...
To help the RL agent learn more general and discriminative representation among various states, we present cross-state self-constraint (CSSC). ...
INTRODUCTION Reinforcement learning (RL) has achieved tremendous success in mastering video games [8] and the game of Go [12] . ...
dblp:conf/atal/LiuLC22
fatcat:e2gtav5ft5b2jkclvsxz5bwwvi
The therapeutic benefits of perceptual learning
2013
Current Trends in Neurology
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reinforcement learning and multisensory facilitation) that allow or restrict learning in the visual system can lead to enhanced treatment approaches. ...
We suggest new approaches that integrate multiple mechanisms of perceptual learning that promise greater learning and more generalization to real world conditions. ...
normal sighted individuals looking for an enhancement of vision. ...
pmid:25580062
pmcid:PMC4286158
fatcat:j7vklwngovbp5glazz7eib2lpe
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation
[article]
2024
arXiv
pre-print
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However, learning representations in RL settings for visuotactile data poses significant challenges, particularly due to the high dimensionality of the data and the complexity involved in correlating visual ...
We evaluate M2CURL on the Tactile Gym 2 simulator and we show that it significantly enhances the learning efficiency in different manipulation tasks. ...
In our study shown in Figure 3 , we compared unimodal (visual-only and tactile-only) reinforcement learning against the multimodal approach in M2CURL. ...
arXiv:2401.17032v1
fatcat:jn3jnv6ctjgrngm25udeahuwcu
Reinforced Swin-Convs Transformer for Underwater Image Enhancement
[article]
2022
arXiv
pre-print
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is reinforced in the channel and the spatial attention of the Swin Transformer. ...
To address problems, a novel U-Net based Reinforced Swin-Convs Transformer for the Underwater Image Enhancement method (URSCT-UIE) is proposed. ...
Underwater Image Enhancement (UIE) technology is invented to improve the quality of the underwater image, categorized into three types in general: physical model-based [7] [8] [9] [10] [11] , visual ...
arXiv:2205.00434v1
fatcat:f4wrx265rjbjjhnvjlvio66saa
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