Avoiding Pitfalls in Neural Network Research
By G. Zhang. 2007
155 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Aunn Raza
- Ala Al-Fuqaha
- Hunain Arif
- Amir Hussain
- Antanas Verikas
- Junaid Qadir
- Zivile Kalsyte
- Marija Bacauskiene
In Management Science and Operations Research
- Evangelos Spiliotis
- Vassilios Assimakopoulos
- Spyros Makridakis
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Ala Al-Fuqaha
- Amir Hussain
- Antanas Verikas
Citations in the corpus
- Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018, background): “As mentioned by [14], it should become clear that ML methods are not a panacea that would automatically improve forecasting accuracy.”
- Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “studying the application and the suitability of the algorithm in such settings [320].”
- Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, background): “In a recent paper, Zhang (2007) discusses the most common pitfalls in using neural networks and suggests guidelines for practitioners.”