An incremental learning classification algorithm based on forgetting factor for eHealth networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The advances of network technology and mobile communication technology are making eHealth possible. In eHealth systems, physiological data and relevant context-aware data are acquired continuously and in real time. At the same time, such large-scale data results in huge challenges in the aspect of real-time big data processing since eHealth data appears in the form of data stream. Therefore, we propose a novel incremental learning algorithm, namely α-SVMSGD, which improves the SVMSGD (Support Vector Machine-Stochastic Gradient Descent) algorithm by updating the training data with the continuous data stream. Besides, this α-SVMSGD may handle the problem that original SVMSGD cannot further mine the useful information in unclassified data. In α-SVMSGD, the process of training data updating is completed by introducing the concept of forgetting mechanism, in which the forgetting factor α is introduced to weed out useless training data. α-SVMSGD is applied into ambient assisted living communications, and further incorporated into the data filtering layer of a local data processing architecture (LDPA) to reduce data redundancy. Simulation results confirm that the proposed algorithm is a promising data redundancy solution for classification without loss of accuracy in the case of real-time data stream.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it