EVALUATION OF SELECTING INTERVAL VALUES OF INPUT VARIABLES IN CONNECTIONIST NETWORKS
Bibliographic record
Abstract
Selecting parameters can be a powerful mechanism in constructing new evolving connectionist network. However, if a parameter contains partial information such that only some of the values are relevant and others are not, then a selection of the subset of relevant values is more appropriate. Considering the possible values of a parameter of a processing connectionist network as the outcomes of a variable, this research focuses on selecting interval values of the variable. It also considers the partitioning schemes used in generating the intervals from the outcomes of a variable. The goal of this work is to explore variable value selection and its effect in an evolving connectionist network. Using input variables in a backpropagation network, the proposed method evaluates its effect based on training of a dataset, and eliminates those intervals of the variable values that contribute negatively when processed by the network. When a value falls into an interval that has been selected and ignored, it is analogous to a network without processing the corresponding variable, and vice versa. Two approaches for interval partitioning are considered, based on equal-probability (or maximum entropy) and equal-width partitioning scheme. Comparing the best performing network with selection and the one without selection, the experimental results show that the best network with selection can produce better performance accuracy and smaller network size.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".