Classifiers in almost empty spaces

By Robert P. W. Duin. 2000

162 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Scaling & Learning Theory, Evaluation & Benchmarks

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

1. Elzbieta Pekalska
2. Pavel Paclík
3. Preetum Nakkiran
4. Boaz Barak
5. Yamini Bansal
6. Robert P. W. Duin
7. Tristan Yang
8. Ilya Sutskever

In Computer Vision and Pattern Recognition

1. Elzbieta Pekalska
2. Pavel Paclík
3. Yamini Bansal
4. Preetum Nakkiran
5. Boaz Barak
6. Tristan Yang
7. Ilya Sutskever
8. Robert P. W. Duin

In Media Technology

1. Elzbieta Pekalska
2. Pavel Paclík
3. Ilya Sutskever
4. Yamini Bansal

In Scaling & Learning Theory

1. Elzbieta Pekalska
2. Pavel Paclík
3. Tristan Yang
4. Boaz Barak
5. Ilya Sutskever
6. Robert P. W. Duin
7. Preetum Nakkiran
8. Yamini Bansal

In Evaluation & Benchmarks

1. Elzbieta Pekalska
2. Pavel Paclík
3. Boaz Barak
4. Ilya Sutskever
5. Robert P. W. Duin
6. Lior Rokach

Citations in the corpus

• Deep double descent: where bigger models and more data hurt (2019, background): “The study of sample non-monotonicity in learning algorithms had also existed prior to double descent, including in Duin (1995; 2000); Opper (2001); Loog & Duin (2012).”
• A Generalized Kernel Approach to Dissimilarity-based Classification (2002, methodology): “In the second approach, a dissimilarity kernel is interpreted as a mapping based on a chosen representation set R of objects (Duin, 2000, P ekalska and Duin, 2001), as presented in Section 5.”