Summary of Mixture Density Networks For Classification with An Application to Product Bundling, by Narendhar Gugulothu et al.
Mixture Density Networks for Classification with an Application to Product Bundling
by Narendhar Gugulothu, Sanjay P. Bhat, Tejas Bodas
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes two Mixture Density Network (MDN)-based models for classification tasks, building on the existing use of MDNs in regression tasks. The proposed models fit mixtures of Gaussians to the data and classify samples by evaluating the learned cumulative distribution function for given input features. Compared to five baseline classification models on three publicly available datasets, the proposed MDNs perform slightly better or similarly well. However, their true utility is demonstrated through a real-world product bundling application, where they learn willingness-to-pay (WTP) distributions from synthetic sales data of individual products and approximate the WTP distribution of the bundled products. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes two new models to help computers classify things correctly. The models use something called Mixture Density Networks, which are good at predicting numbers, but not as well-known for classifying things into groups. The researchers tested their models on three different sets of data and found they worked just as well or a little better than other popular classification methods. But what’s really cool is that the models can be used in real-life situations, like when you’re trying to decide whether someone will buy two products together or separately. |
Keywords
* Artificial intelligence * Classification * Regression
