# Machine Learning

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/machine-learning/

## Facts

| Field | Value |
| --- | --- |
| APC (USD) | 2,990 |
| Citations | 636,628 |
| h-index | 262 |
| Homepage | http://www.springer.com/west/home/computer/artificial?SGWID=4-147-70-35726603-0 |
| Open Access | false |
| ISSN-L | 0885-6125 |
| ISSNs | 0885-6125,1573-0565 |
| OpenAlex ID | https://openalex.org/S62148650 |
| Publisher | Springer Science+Business Media |
| Works | 4,033 |

## Journal papers

- [A Bayesian Method for the Induction of Probabilistic Networks from Data](https://scholariq.org/papers/a-bayesian-method-for-the-induction-of-probabilistic-networks-from-data/)
- [A Bayesian method for the induction of probabilistic networks from data](https://scholariq.org/papers/a-bayesian-method-for-the-induction-of-probabilistic-networks-from-data-2/)
- [Principal component analysis](https://scholariq.org/papers/principal-component-analysis/)
- [Linear regression](https://scholariq.org/papers/linear-regression/)
- [Bayesian policy reuse](https://scholariq.org/papers/bayesian-policy-reuse/)

## Journal top topics

Showing 8 of 25.

- [Machine Learning and Algorithms](https://scholariq.org/topics/machine-learning-and-algorithms/)
- [Machine Learning and Data Classification](https://scholariq.org/topics/machine-learning-and-data-classification/)
- [Neural Networks and Applications](https://scholariq.org/topics/neural-networks-and-applications/)
- [Data Mining Algorithms and Applications](https://scholariq.org/topics/data-mining-algorithms-and-applications/)
- [Bayesian Modeling and Causal Inference](https://scholariq.org/topics/bayesian-modeling-and-causal-inference/)
- [Imbalanced Data Classification Techniques](https://scholariq.org/topics/imbalanced-data-classification-techniques/)
- [Face and Expression Recognition](https://scholariq.org/topics/face-and-expression-recognition/)
- [Reinforcement Learning in Robotics](https://scholariq.org/topics/reinforcement-learning-in-robotics/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.