Reorganing References

parent 7893ff7b
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in 38 minutes and 59 seconds
......@@ -19,7 +19,7 @@ static auto PLDABase_doc = bob::extension::ClassDoc(
"This class is a container for the :math:`F` (between class variantion matrix), :math:`G` (within class variantion matrix) and :math:`\\Sigma` "
"matrices and the mean vector :math:`\\mu` of a PLDA model. This also"
"precomputes useful matrices to make the model scalable."
"References: [ElShafey2014,PrinceElder2007,LiFu2012]",
"References: [ElShafey2014]_ [PrinceElder2007]_ [LiFu2012]_ ",
""
).add_constructor(
bob::extension::FunctionDoc(
......
......@@ -20,7 +20,7 @@ static auto PLDAMachine_doc = bob::extension::ClassDoc(
"This class is a container for an enrolled identity/class. It contains information extracted from the enrollment samples. "
"It should be used in combination with a PLDABase instance.\n\n"
"References: [ElShafey2014]_, [PrinceElder2007]_, [LiFu2012]_",
"References: [ElShafey2014]_ [PrinceElder2007]_ [LiFu2012]_ ",
""
).add_constructor(
bob::extension::FunctionDoc(
......
......@@ -106,7 +106,7 @@ static auto PLDATrainer_doc = bob::extension::ClassDoc(
BOB_EXT_MODULE_PREFIX ".PLDATrainer",
"This class can be used to train the :math:`F`, :math:`G` and "
" :math:`\\Sigma` matrices and the mean vector :math:`\\mu` of a PLDA model."
"References: [ElShafey2014]_,[PrinceElder2007]_,[LiFu2012]_",
"References: [ElShafey2014]_ [PrinceElder2007]_ [LiFu2012]_ ",
""
).add_constructor(
bob::extension::FunctionDoc(
......
......@@ -121,7 +121,7 @@ Maximum likelihood Estimator (MLE)
In statistics, maximum likelihood estimation (MLE) is a method of estimating
the parameters of a statistical model given observations by finding the
:math:`\Theta` that maximizes :math:`P(x|\Theta)` for all :math:`x` in your
dataset [10]_. This optimization is done by the **Expectation-Maximization**
dataset [9]_. This optimization is done by the **Expectation-Maximization**
(EM) algorithm [8]_ and it is implemented by
:py:class:`bob.learn.em.ML_GMMTrainer`.
......@@ -181,7 +181,7 @@ Maximum a posteriori Estimator (MAP)
Closely related to the MLE, Maximum a posteriori probability (MAP) is an
estimate that equals the mode of the posterior distribution by incorporating in
its loss function a prior distribution [11]_. Commonly this prior distribution
its loss function a prior distribution [10]_. Commonly this prior distribution
(the values of :math:`\Theta`) is estimated with MLE. This optimization is done
by the **Expectation-Maximization** (EM) algorithm [8]_ and it is implemented
by :py:class:`bob.learn.em.MAP_GMMTrainer`.
......@@ -582,7 +582,7 @@ The snippet bellow shows how to compute scores using this approximation.
Probabilistic Linear Discriminant Analysis (PLDA)
-------------------------------------------------
Probabilistic Linear Discriminant Analysis [16]_ is a probabilistic model that
Probabilistic Linear Discriminant Analysis [5]_ is a probabilistic model that
incorporates components describing both between-class and within-class
variations. Given a mean :math:`\mu`, between-class and within-class subspaces
:math:`F` and :math:`G` and residual noise :math:`\epsilon` with zero mean and
......@@ -598,7 +598,7 @@ An Expectation-Maximization algorithm can be used to learn the parameters of
this model :math:`\mu`, :math:`F` :math:`G` and :math:`\Sigma`. As these
parameters can be shared between classes, there is a specific container class
for this purpose, which is :py:class:`bob.learn.em.PLDABase`. The process is
described in detail in [17]_.
described in detail in [6]_.
Let us consider a training set of two classes, each with 3 samples of
dimensionality 3.
......@@ -793,9 +793,11 @@ Follow bellow an example of score normalization using
.. [2] http://publications.idiap.ch/index.php/publications/show/2606
.. [3] http://dx.doi.org/10.1016/j.csl.2007.05.003
.. [4] http://dx.doi.org/10.1109/TASL.2010.2064307
.. [5] http://dx.doi.org/10.1109/ICCV.2007.4409052
.. [6] http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.38
.. [7] http://en.wikipedia.org/wiki/K-means_clustering
.. [8] http://en.wikipedia.org/wiki/Expectation-maximization_algorithm
.. [10] http://en.wikipedia.org/wiki/Maximum_likelihood
.. [11] http://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation
.. [16] http://dx.doi.org/10.1109/ICCV.2007.4409052
.. [17] http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.38
.. [9] http://en.wikipedia.org/wiki/Maximum_likelihood
.. [10] http://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation
......@@ -32,18 +32,26 @@ References
-----------
.. [Reynolds2000] *Reynolds, Douglas A., Thomas F. Quatieri, and Robert B. Dunn*. **Speaker Verification Using Adapted Gaussian Mixture Models**, Digital signal processing 10.1 (2000): 19-41.
.. [Vogt2008] *R. Vogt, S. Sridharan*. **'Explicit Modelling of Session Variability for Speaker Verification'**, Computer Speech & Language, 2008, vol. 22, no. 1, pp. 17-38
.. [McCool2013] *C. McCool, R. Wallace, M. McLaren, L. El Shafey, S. Marcel*. **'Session Variability Modelling for Face Authentication'**, IET Biometrics, 2013
.. [ElShafey2014] *Laurent El Shafey, Chris McCool, Roy Wallace, Sebastien Marcel*. **'A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition'**, TPAMI'2014
.. [PrinceElder2007] *Prince and Elder*. **'Probabilistic Linear Discriminant Analysis for Inference About Identity'**, ICCV'2007
.. [LiFu2012] *Li, Fu, Mohammed, Elder and Prince*. **'Probabilistic Models for Inference about Identity'**, TPAMI'2012
.. [Bishop1999] Tipping, Michael E., and Christopher M. Bishop. "Probabilistic principal component analysis." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61.3 (1999): 611-622.
.. [Roweis1998] Roweis, Sam. "EM algorithms for PCA and SPCA." Advances in neural information processing systems (1998): 626-632.
.. [Glembek2009] Glembek, Ondrej, et al. "Comparison of scoring methods used in speaker recognition with joint factor analysis." Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. IEEE, 2009.
.. [Auckenthaler2000] Auckenthaler, Roland, Michael Carey, and Harvey Lloyd-Thomas. "Score normalization for text-independent speaker verification systems." Digital Signal Processing 10.1 (2000): 42-54.
.. [Mariethoz2005] Mariethoz, Johnny, and Samy Bengio. "A unified framework for score normalization techniques applied to text-independent speaker verification." IEEE signal processing letters 12.7 (2005): 532-535.
.. [Vogt2008] *R. Vogt, S. Sridharan*. **'Explicit Modelling of Session Variability for Speaker Verification'**, Computer Speech & Language, 2008, vol. 22, no. 1, pp. 17-38
.. [McCool2013] *C. McCool, R. Wallace, M. McLaren, L. El Shafey, S. Marcel*. **'Session Variability Modelling for Face Authentication'**, IET Biometrics, 2013
.. [ElShafey2014] *Laurent El Shafey, Chris McCool, Roy Wallace, Sebastien Marcel*. **'A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition'**, TPAMI'2014
.. [PrinceElder2007] *Prince and Elder*. **'Probabilistic Linear Discriminant Analysis for Inference About Identity'**, ICCV'2007
.. [LiFu2012] *Li, Fu, Mohammed, Elder and Prince*. **'Probabilistic Models for Inference about Identity'**, TPAMI'2012
.. [Bishop1999] Tipping, Michael E., and Christopher M. Bishop. "Probabilistic principal component analysis." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61.3 (1999): 611-622.
.. [Roweis1998] Roweis, Sam. "EM algorithms for PCA and SPCA." Advances in neural information processing systems (1998): 626-632.
.. [Glembek2009] Glembek, Ondrej, et al. "Comparison of scoring methods used in speaker recognition with joint factor analysis." Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. IEEE, 2009.
.. [Auckenthaler2000] Auckenthaler, Roland, Michael Carey, and Harvey Lloyd-Thomas. "Score normalization for text-independent speaker verification systems." Digital Signal Processing 10.1 (2000): 42-54.
.. [Mariethoz2005] Mariethoz, Johnny, and Samy Bengio. "A unified framework for score normalization techniques applied to text-independent speaker verification." IEEE signal processing letters 12.7 (2005): 532-535.
Indices and tables
......
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