An Approach to Supporting Incremental Visual Data Classification

By J. G. Paiva, William Robson Schwartz, Hélio Pedrini, Rosane Minghim. 2015

63 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jun Zhu
2. Chongxuan Li
3. Jiaxin Shi
4. Z. Li
5. Mengchen Liu
6. Shixia Liu
7. Xiting Wang
8. Ashish Kapoor

Citations in the corpus

• Towards Better Analysis of Deep Convolutional Neural Networks (2016, background): “In the field of visual analytics, researchers have developed interactive image classification systems [3, 47, 51].”
• Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models (2018, methodology): “Typical solutions include improving hyperparameters [9,13], features [6,26,43], or training instances [29] and investigating multiple models in order to acquire an optimal model ensemble [35,38,43].”
• FeatureInsight: Visual support for error-driven feature ideation in text classification (2015, background): “While the literature includes examples of visual, interactive data classification [19], [20], few systems help users directly create features.”
• Towards better analysis of machine learning models: A visual analytics perspective (2017, methodology): “Many techniques have been developed to help experts better understand classification models [15, 16, 17] and regression models [18].”