Latent Support Measure Machines for Bag-of-Words Data Classification
By Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada. 2014
32 citations recorded by the source corpus.
Subfields: Natural Language Processing, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Danica J. Sutherland
- Jeff Schneider
- Hiroshi Sawada
- Tomoharu Iwata
- Yuya Yoshikawa
- Kenji Fukumizu
- Bernhard Schölkopf
- Le Song
In Representation & Self-Supervised Learning
- Bernhard Schölkopf
- Kenji Fukumizu
- Bharath K. Sriperumbudur
- Krikamol Muandet
- Yu Nishiyama
- Le Song
- Arthur Gretton
- Tomoharu Iwata
Citations in the corpus
- Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “This is ubiquitous in, for example, multiple-instance learning (Doran 2013), learning with noisy and uncertain input, learning from missing data, group anomaly detection (Muandet and Schölkopf 2013, Guevara et al. 2015), dataset squishing, and bag-of-words data (Yoshikawa et al. 2014; 2015).”
- On the Error of Random Fourier Features (2015, methodology): “2012) or documents as sets of word descriptors (Yoshikawa et al. 2014).”
- Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model (2015, methodology): “The latent support measure machine is a generalization of the support measure machine, which can classify bag-of-words data (Yoshikawa, Iwata, and Sawada 2014).”
- Proposal : Scalable , Active and Flexible Learning on Distributions (2015, unknown): “One approach would be to choose these vectors arbitrarily, optimizing them for the output of some learning problem: this would be implemantionally similar to the approach of Yoshikawa et al. (2014, 2015) for mmd distances, or somewhat like that of Vilnis and McCallum (2015) but with greater computat”
- The Nonparametric Kernel Bayes Smoother (2016, methodology): “This embedding approach has several benefits over previous nonparametric methods: (i) closeness and similarity in the set of probability distributions is introduced via the RKHS norm, which is useful for various machine learning algorithms [12, 11, 9, 20, 30]; and (ii) estimation is relatively easy ”