Results 121 to 130 of about 3,294 (168)

The Fisher-Rao Distance between Multivariate Normal Distributions: Special Cases, Bounds and Applications. [PDF]

Entropy (Basel), 2020
Pinele J, Strapasson JE, Costa SIR.
europepmc   +1 more source

Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models. [PDF]

J Cheminform
Friesacher HR, Engkvist O, Mervin L, Moreau Y, Arany A.   +4 more
europepmc   +1 more source

Principal Curves for Statistical Divergences and an Application to Finance. [PDF]

Entropy (Basel), 2018
Rodrigues AFP, Cavalcante CC.
europepmc   +1 more source

Information Theory Meets Quantum Chemistry: A Review and Perspective. [PDF]

Entropy (Basel)
Zhao Y, Zhao D, Rong C, Liu S, Ayers PW.
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A Quantitative Measurement Method for Nuclear-Pleomorphism Scoring in Breast Cancer. [PDF]

Diagnostics (Basel)
Teoh CL, Tan XJ, Ab Rahman KS, Bakrin IH, Goh KM, Siet JJW, Wan Muhamad WZA.   +6 more
europepmc   +1 more source

Information Geometry for Covariance Estimation in Heterogeneous Clutter with Total Bregman Divergence. [PDF]

Entropy (Basel), 2018
Hua X, Cheng Y, Wang H, Qin Y.
europepmc   +1 more source

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Functional Bregman divergence

2008 IEEE International Symposium on Information Theory, 2008
To characterize the differences between two positive functions or two distributions, a class of distortion functions has recently been defined termed the functional Bregman divergences. The class generalizes the standard Bregman divergence defined for vectors, and includes total squared difference and relative entropy.
Bela A. Frigyik, Santosh Srivastava, Maya R. Gupta   +2 more
openaire   +1 more source

Clustering with Bregman Divergences

Proceedings of the 2004 SIAM International Conference on Data Mining, 2004
A wide variety of distortion functions, such as squared Euclidean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been used for clustering. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences.
Arindam Banerjee, Srujana Merugu, Inderjit Dhillon, Joydeep Ghosh   +3 more
openaire   +1 more source

Cost-Sensitive Sequences of Bregman Divergences

IEEE Transactions on Neural Networks and Learning Systems, 2012
The minimization of the empirical risk based on an arbitrary Bregman divergence is known to provide posterior class probability estimates in classification problems, but the accuracy of the estimate for a given value of the true posterior depends on the specific choice of the divergence.
Santos-Rodriguez, Raúl, Cid-Sueiro, Jesús   +1 more
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Bregman Divergences and Surrogates for Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009
Bartlett et al. (2006) recently proved that a ground condition for surrogates, classification calibration, ties up their consistent minimization to that of the classification risk, and left as an important problem the algorithmic questions about their minimization.
Richard, Nock, Frank, Nielsen
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