<title>New strategy for adaptively constructing multilayer feed-forward neural networks</title>
Bibliographic record
Abstract
It is quite well-known that one-hidden-layer feed-forward neural networks (FNNs) can approximate any continuous function to any desired accuracy as long as enough hidden units are included. Due to this fact many developments in constructive neural networks have been concentrated on only constructive or adaptive one-hidden-layer FNNs. However, this fact does not necessarily imply that one-hidden-layer networks are the most efficient and the best network structure feasible, as one has no explicit guideline to properly select the network structure. Consequently, in practice it has been observed that networks with more than one hidden layer may perform better than the one-hidden- layer networks in some applications. In this paper, we propose a novel strategy for constructing a multi-hidden- layer FNN with regular connections. The new algorithm incorporates in part the policy for adding hidden units from a one-hidden-layer constructive algorithm, and has in part its own new policy for additional layer creation. Extensive simulations are performed for nonlinear noisy regression problems, and it is found that the proposed algorithm converges quite fast and produces networks with one or as many hidden layers/units as required, which are dictated by the complexity of the underlying problem.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".