Incremental Deep Computation Model for Wireless Big Data Feature Learning
Bibliographic record
Abstract
Big data feature learning is a crucial issue for the service management for Internet of Things. However, big data collected from Internet of Things is of dynamic nature at a high speed, which poses an important challenge on wireless big data learning models, especially the deep computation model. In this paper, an incremental deep computation model is proposed for wireless big data feature learning in Internet of Things. First, two incremental tensor auto-encoders (ITAE) are developed by devising two incremental learning algorithms, namely parameter-based incremental learning algorithm (PI-TAE) and structure-based incremental learning algorithm (SI-TAE), when new wireless samples are available. PI-TAE only updates the network parameters while SI-TAE simultaneously adjusts the structure and updates the parameters to adapt to the new arriving wireless big data. Furthermore, an incremental deep computation model is constructed by stacking several ITAEs. Experiments are conducted to evaluate the performance of the proposed model by comparing with the conventional deep computation model and other two representative incremental learning algorithms, i.e., OANN and PIE. Results demonstrate that the presented model can modify the network in an incremental manner for new arriving data learning efficiently with preserving the prior knowledge for the previous data learning, proving its potential for dynamic wireless big data learning in Internet of Things.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".