Bibliographic record
Abstract
Learning without thought is labor lost; thought without learning is perilous . Confucius (551 BC – 479 BC), The Confucian Analects This chapter goes beyond the supervised learning of Chapter 7. It covers learning richer representation and learning what to do; this enables learning to be combined with reasoning. First we consider unsupervised learning in which the classifications are not given in the training set. This is a special case of learning belief network, which is considered next. Finally, we consider reinforcement learning, in which an agent learns how to act while interacting with an environment. Clustering Chapter 7 considered supervised learning, where the target features that must be predicted from input features are observed in the training data. In clustering or unsupervised learning , the target features are not given in the training examples. The aim is to construct a natural classification that can be used to cluster the data. The general idea behind clustering is to partition the examples into clusters or classes . Each class predicts feature values for the examples in the class. Each clustering has a prediction error on the predictions. The best clustering is the one that minimizes the error. Example 11.1 A diagnostic assistant may want to group the different treatments into groups that predict the desirable and undesirable effects of the treatment. The assistant may not want to give a patient a drug because similar drugs may have had disastrous effects on similar patients. […]
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".