updated docs

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Davis King 2011-01-03 20:46:26 +00:00
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<a href="dlib/matrix/matrix_la_abstract.h.html#svd">singular value decomposition</a>,
<a href="dlib/matrix/matrix_utilities_abstract.h.html#trans">transpose</a>,
<a href="dlib/matrix/matrix_math_functions_abstract.h.html#sin">trig functions</a>, etc.</li>
<li>Unconstrained non-linear optimization algorithms using the
<li>General purpose unconstrained non-linear optimization algorithms using the
<a href="optimization.html#cg_search_strategy">conjugate gradient</a>,
<a href="optimization.html#bfgs_search_strategy">BFGS</a>,
<a href="optimization.html#lbfgs_search_strategy">L-BFGS</a>, and
<a href="optimization.html#solve_least_squares_lm">Levenberg-Marquardt</a>
<a href="optimization.html#bfgs_search_strategy">BFGS</a>, and
<a href="optimization.html#lbfgs_search_strategy">L-BFGS</a>
techniques</li>
<li> <a href="optimization.html#solve_least_squares_lm">Levenberg-Marquardt</a> for solving non-linear
least squares problems </li>
<li>Box-constrained derivative-free optimization via the
<a href="optimization.html#find_min_bobyqa">BOBYQA</a> algorithm</li>
<li>An implementation of the <a href="optimization.html#oca">Optimized Cutting Plane Algorithm</a></li>
<li>Several <a href="optimization.html#solve_qp2_using_smo">quadratic</a>
<a href="optimization.html#solve_qp3_using_smo">program</a>
<a href="optimization.html#solve_qp_using_smo">solvers</a> </li>
<li>A <a href="algorithms.html#bigint">big integer</a> object</li>
<li>A <a href="algorithms.html#rand">random number</a> object</li>
</ul>
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<li><b>Machine Learning Algorithms</b>
<ul>
<li>Conventional SMO based <a href="ml.html#svm_nu_trainer">Support vector machines</a> for classification</li>
<li>Conventional SMO based Support Vector Machines for <a href="ml.html#svm_nu_trainer">classification</a>
and <a href="ml.html#svr_trainer">regression</a> </li>
<li>Reduced-rank methods for large-scale <a href="ml.html#svm_c_ekm_trainer">classification</a>
and <a href="ml.html#krr_trainer">regression</a></li>
<li>Relevance vector machines for <a href="ml.html#rvm_trainer">classification</a>
and <a href="ml.html#rvm_regression_trainer">regression</a> </li>
<li>General purpose <a href="ml.html#one_vs_one_trainer">multiclass classification</a> tools</li>
<li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>
<li>An online <a href="ml.html#svm_pegasos">SVM classification</a> algorithm</li>
<li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector</li>
<li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector</li> and
offline support vector <a href="ml.html#svm_one_class_trainer">one-class classification</a>
<li>A kernelized <a href="ml.html#kkmeans">k-means</a> clustering algorithm</li>
<li><a href="ml.html#rbf_network_trainer">Radial Basis Function Networks</a></li>
<li><a href="ml.html#mlp">Multi layer perceptrons</a> </li>

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<current>
New Stuff:
- General Stuff
- Added the promote template
- Added the basic_type template
- Added the assign_image_scaled() function
- Added the unordered_pair object.
- Added the symmetric_matrix_cache() function
- Added two new quadratic program solvers. The solve_qp2_using_smo
and solve_qp3_using_smo objects.
- Machine Learning Stuff
- Added the svm_c_trainer object. It implements C-SVM classification and
allows the user to set different C values for each class.
- Added the svm_one_class_trainer object.
- Added the svr_trainer object. It implements epsilon-insensitive
support vector regression.
- Added two new any objects. The any_decision_function for containing
decision function style objects and the any_trainer for trainers.
- Added cross_validate_regression_trainer()
- Added test_regression_function()
- Added the probabilistic() function. It is a trainer adapter that
simply calls train_probabilistic_decision_function().
- Added tools for multiclass classification
- Added one_vs_one_trainer
- Added one_vs_all_trainer
- Added cross_validate_multiclass_trainer()
- Added test_multiclass_decision_function()
Non-Backwards Compatible Changes:
- invalid_svm_nu_error has been renamed to invalid_nu_error.
- Changed the pixel_traits so that signed grayscale pixel types are allowed.
This involved adding a few new fields to pixel_traits. I also changed the
get_pixel_intensity() function so that its return value is of the same type
as the basic pixel type rather than always being unsigned long.
- Removed the kernel_type typedef from the normalized function since this
meta-object should be capable of working with non-kernel decision functions.
- train_probabilistic_decision_function() no longer accepts column vectors of
samples and labels. Now it only accepts std::vectors of samples and labels.
Bug fixes:
- Fixed a bug in the deserialization code for the sparse kernels. The bug
prevented code which used the deserialize() routine from compiling.
Other:
- Changed the image display GUI widgets to use the assign_image_scaled()
function internally. Now they will accept just about any image and
do the right thing.
- Modified the type_safe_union so that you can call apply_to_contents() on const
type_safe_unions.
- Added serialization support for std::pair objects.
- Made the train_probabilistic_decision_function() more general by making it work
with any kind of trainer object rather than only ones which produce
dlib::decision_function objects. I also made it work with trainers that only
take std::vectors.
- Added overloads to the config_reader's methods to allow it to load directly
from a file name given as a string in addition to taking istream objects.
</current>
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