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SoftMax Inc alexnet softmax
The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in <t>AlexNet</t> ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.
Alexnet Softmax, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/alexnet++softmax/alexnet+softmax/pmc04750055-17-0-2
Average 90 stars, based on 1 article reviews
alexnet softmax - by Bioz Stars, 2026-10
90/100 stars

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1) Product Images from "Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network"

Article Title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

Journal: Scientific Reports

doi: 10.1038/srep20410

The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in AlexNet ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.
Figure Legend Snippet: The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in AlexNet ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.

Techniques Used: Biomarker Discovery

Comparison of DCNNs with other methods on the same dataset.
Figure Legend Snippet: Comparison of DCNNs with other methods on the same dataset.

Techniques Used: Comparison

Related Articles

other:

Article Title: Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology
Article Snippet: 109 , Senan et al. [ ] (2022) , H vs. MN vs. GL vs. PT , Hold-out , 80:20 , - , AlexNet-SoftMax AlexNet-SVM ResNet18-SoftMax ResNet18-SVM , 93.30 95.10 93.80 91.20 , - - - - , - - - - , H = 91.10, MN = 89.80, GL = 93.30, PT = 97.80 H = 91.10, MN = 89.80, GL = 93.30, PT = 97.80 H = 94.90, MN = 93.60, GL = 93.90, PT = 97.80 H = 87.30, MN = 93.60, GL = 93.30, PT = 97.20 H = 92.40, MN = 86.10, GL = 91.50, PT = 95.60.

Article Title: Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet.
Article Snippet: Model TP FP FN PrecisionRate Recall Rate F1-Measure Modified AlexNet-SVM 185 2 0 98.93% 100% 99.46% Modified AlexNet-Softmax 183 3 1 96.51% 98.81% 98.92% Modified AlexNet-kNN 186 3 0 98.41% 100% 99.20% Sensors 2023, 23, x FOR PEER REVIEW 14 of 18 Figure 12.

Article Title: Applied Artificial Intelligence and Prospect of Internet of Everything (IoE) for the Detection of Tuberculosis
Article Snippet: The result of the model performance shows that AlexNet+SoftMax achieved 98.19% accuracy, 99.62% sensitivity and 96.76% specificity on unseen dataset, while AlexNet+SVM achieved 98.38% accuracy, 98.71% sensitivity and 98.04% specificity on unseen dataset.

Article Title: Large‐scaled detection of COVID‐19 from X‐ray using transfer learning
Article Snippet: Correspondence Abdullahi Umar Ibrahim, Department of Biomedical Engineering, Near East University, Nicosia, Mersin 10, Turkey.. Email: abdullahi.umaribrahim@neu. edu.tr Abstract The pandemic cause as a result of the outbreak of COVID-19 disease continues to burden the healthcare system despite several interventions using vaccines and other preventive measures.. Healthcare settings adopted the use of reverse transcription-polymerase chain reaction (RT-PCR) which is hampered by so many challenges such as miss-diagnosis, false positive results, high cost, especially for those in remote and rural areas, the need for trained medical pathologists, the use of chemicals, and a lack of point-of-care detection.



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