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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Correction: Zhang et al. Caudate-Centric Triphasic Network Reconfiguration Characterizes the Early Progression of Cognitive Impairment in Parkinson's Disease: A Simultaneous PET/fMRI Study. Journal of Integrative Neuroscience. 2026; 25(2): 46634.

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The Role of Knowledge Creation-Oriented Convolutional Neural Network in Learning Interaction.

Hongyan Zhang1,2, Xiaoguang Luo1

  • 1School of Economics and Management, Harbin University of Science and Technology, Harbin, Heilongjiang, China.

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Summary
This summary is machine-generated.

This study introduces an improved ResNet deep transfer learning method to enhance image recognition accuracy by extracting high-order statistical features. The method significantly boosts performance on MNIST and CIFAR-10 datasets, addressing challenges in domain differences for convolutional neural networks (CNNs).

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional convolutional neural network (CNN) methods struggle with domain differences in transfer learning tasks, leading to suboptimal image feature recognition.
  • Existing approaches often overlook the discrepancies between source and target domains, even when labeled data is available.

Purpose of the Study:

  • To propose a novel deep transfer learning method using an improved ResNet architecture to overcome limitations in CNNs with differing source and target domain tasks.
  • To enhance the extraction of high-order statistical features for improved image classification accuracy in transfer learning scenarios.

Main Methods:

  • Implemented a deep transfer learning approach based on an improved ResNet architecture, leveraging the ImageNet dataset as the source domain.
  • Pretrained the ResNet model, modified its final fully connected layer, and fine-tuned the network with an adjustment module for model transfer.
  • Compared the improved ResNet method against Support Vector Machine (SVM), VGG-19, and Inception-V3 deep transfer learning methods on MNIST and CIFAR-10 datasets.

Main Results:

  • The improved ResNet deep transfer learning method achieved high accuracy rates: 97.98% on MNIST and 90.45% on CIFAR-10 for training sets.
  • The method demonstrated strong performance on test sets with 95.33% accuracy on MNIST and 85.07% on CIFAR-10.
  • Experimental data confirmed significant improvements in both accuracy and recognition accuracy compared to other deep transfer learning methods.

Conclusions:

  • The proposed improved ResNet deep transfer learning method effectively mitigates the impact of dataset content differences on feature recognition.
  • Combining CNNs with transfer learning offers a significant advantage in alleviating the challenges associated with acquiring labeled data.
  • The study underscores the importance and effectiveness of CNNs in transfer learning applications for improved image analysis.