Probabilities for SV Machines

490 citations recorded by the source corpus.

How researchers see this paper

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

The challenge problem for automated detection of 101 semantic concepts in multimedia (2006, methodology): “We prefer Platt’s conversion method [34] to achieve a confidence score.”
Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration (2017, methodology): “Logistic calibration was proposed by (Platt, 2000) to reduce overﬁtting by introducing a strong inductive bias which considers only calibration maps of the following form: where γ, δ are real-valued parameters with γ ≥ 0 to ensure that the calibration map is monotonically non-decreasing.”
RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation (2008, methodology): “Predictions were performed using a soft margin SVM with a linear kernel and reported as empirical probabilities via a sig-moidal mapping 17 as implemented in the R package e1071.”
Co-Training for Domain Adaptation (2011, methodology): “In logistic regression, if ŷ = sign(h(x)) is the prediction for an input x, the probability Ph(Y = ŷ|X = x;w) is a natural metric of certainty (as h(x) can be interpreted as a probability for x to be of label +1), but other methods [22] can be used.”