Torrent details for "Zhang C. Advanced Randomized Neural Networks for Pattern Analysi…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
10.4 MB
Info Hash:
2998B0BC4475117A92ACE7909F51EE286A3C4BAC
Added By:
Added:
Oct. 27, 2025, 9:31 a.m.
Stats:
|
(Last updated: Oct. 27, 2025, 9:33 a.m.)
| File | Size |
|---|---|
| Zhang C. Advanced Randomized Neural Networks for Pattern Analysis 2026.pdf | 10.4 MB |
Name
DL
Uploader
Size
S/L
Added
-
26.6 MB
[12
/
15]
2023-07-02
| Uploaded by indexFroggy | Size 26.6 MB | Health [ 12 /15 ] | Added 2023-07-02 |
-
19.9 MB
[12
/
3]
2024-07-07
| Uploaded by indexFroggy | Size 19.9 MB | Health [ 12 /3 ] | Added 2024-07-07 |
-
22.9 MB
[17
/
3]
2024-12-25
| Uploaded by indexFroggy | Size 22.9 MB | Health [ 17 /3 ] | Added 2024-12-25 |
-
29.0 MB
[23
/
3]
2024-12-25
| Uploaded by indexFroggy | Size 29.0 MB | Health [ 23 /3 ] | Added 2024-12-25 |
-
10.4 MB
[21
/
14]
2025-10-27
| Uploaded by andryold1 | Size 10.4 MB | Health [ 21 /14 ] | Added 2025-10-27 |
NOTE
SOURCE: Zhang C. Advanced Randomized Neural Networks for Pattern Analysis 2026
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format This book is the culmination of our research in the recent decade on randomized neural networks with data-dependent supervision mechanisms. Traditional randomized neural networks mainly focused on constructing various deep neural networks with data independent random weights, ignoring the impact of the number of nodes and scope of parameters on the universal approximation property (UAP) of randomized neural networks. Comprising of 15 chapters, Advanced Randomized Neural Networks for Pattern Analysis introduces systematic solutions for advanced data-dependent stochastic configuration networks, namely algorithms that assign random parameters and construct network structures incrementally. The book is segmented into three major sections — neural networks optimization, robust data analysis, and deep fusion learning — that feature the successful performance of advanced randomized neural networks in various pattern analysis problems. We anticipate that both researchers and engineers in the field of artificial neural networks, particularly pattern recognition and medical diagnosis, will find this book and the associated algorithms useful, and we hope that anyone with an interest in the related research field will find the book enjoyable and informative. Preface Neural Networks Optimization: Decay Regularized Stochastic Configuration Network with Multi-Level Signal Processing Regularized Stochastic Configuration Network Based on Weighted Mean of Vectors Stochastic Configuration Networks with Group Lasso Regularization Greedy Stochastic Configuration Networks for Ill-Posed Problems Robust Data Analysis: Intuitionistic Fuzzy Stochastic Configuration Networks Weighted Deep Stochastic Configuration Networks Based on M-Estimator Functions Noise Robust Regularized Deep Stochastic Configuration Networks Robust Semi-Supervised Stochastic Configuration Network Deep Fusion Learning: Deep Stochastic Configuration Networks Ensemble via Hyper-Parameter Optimization Deep Stochastic Configuration Networks Ensemble via Boosting Negative Correlation Learning Ensemble Intuitionistic Fuzzy Deep Stochastic Configuration Network Stacked Deep Stochastic Configuration Networks with Multi-Level Feature Fusion Stochastic Configuration Network with Long Short-Term Memory Feature Embedding Book Review and Future Work
×