| /external/libopus/silk/float/ |
| residual_energy_FLP.c | 47 silk_float tmp, nrg = 0.0f, regularization; local
52 regularization = REGULARIZATION_FACTOR * ( wXX[ 0 ] + wXX[ D * D - 1 ] );
75 matrix_c_ptr( wXX, i, i, D ) += regularization;
78 regularization *= 2.0f;
|
| /external/ceres-solver/internal/ceres/ |
| schur_complement_solver_test.cc | 88 bool regularization,
109 if (regularization) {
115 if (regularization) {
|
| dogleg_strategy.h | 108 // the next solve starts with a stronger regularization.
145 // and the regularization used to do the Gauss-Newton solve is
|
| implicit_schur_complement_test.cc | 191 // We do this with and without regularization to check that the
|
| minimizer.h | 127 // regularization making the linear least squares problem better
|
| dogleg_strategy.cc | 624 // Reduce the regularization multiplier, in the hope that whatever
|
| schur_eliminator_impl.h | 587 // typically arise from regularization terms in the original
|
| /external/ceres-solver/examples/ |
| fields_of_experts.h | 32 // model. The Fields of Experts regularization consists of terms of the type
75 // The loss function used to build the correct regularization. See above.
|
| denoising.cc | 105 // Create Ceres cost and loss functions for regularization. One is needed for
114 // Add FoE regularization for each patch in the image.
|
| ellipse_approximation.cc | 427 // Add regularization to minimize the length of the line segment contour.
|
| /external/opencv3/modules/shape/include/opencv2/shape/ |
| shape_transformer.hpp | 101 /** @brief Set the regularization parameter for relaxing the exact interpolation requirements of the TPS
104 @param beta value of the regularization parameter.
|
| /frameworks/ml/bordeaux/learning/stochastic_linear_ranker/jni/ |
| jni_stochastic_linear_ranker.h | 33 /* Ddetermines type of the regularization used in learning.
34 This regularization can be based on different norms.
|
| jni_stochastic_linear_ranker.cpp | 129 ALOGE("Error: %s is not a Regularization Type", cValue);
|
| /external/opencv3/modules/shape/src/ |
| tps_trans.cpp | 85 << "regularization" << regularizationParameter;
91 regularizationParameter = (int)fn["regularization"];
|
| sc_dis.cpp | 237 // regularization parameter with annealing rate annRate //
|
| /external/libopus/silk/ |
| tuning_parameters.h | 53 /* LPC analysis regularization */
|
| /frameworks/ml/bordeaux/learning/stochastic_linear_ranker/native/ |
| stochastic_linear_ranker.h | 234 // Note that a form of L2 regularization is built into this
|
| sparse_weight_vector.cpp | 374 ALOGE("Unsupported regularization type requested");
|
| /external/ceres-solver/include/ceres/ |
| iteration_callback.h | 117 // the Levenberg-Marquardt algorithm, the regularization parameter
|
| /external/opencv3/modules/cudaoptflow/include/opencv2/ |
| cudaoptflow.hpp | 282 * It serves as a link between the attachment and the regularization terms.
|
| /external/opencv3/modules/ml/doc/ |
| ml_intro.markdown | 464 - In order to compensate for overfitting regularization is performed, which can be enabled with
466 kind of regularization has to be performed by passing one of @ref
467 cv::ml::LogisticRegression::RegKinds "regularization kinds" to this method.
|
| /external/opencv3/modules/ml/src/ |
| lr.cpp | 93 CV_IMPL_PROPERTY(int, Regularization, params.norm)
|
| /external/chromium-trace/catapult/third_party/gsutil/third_party/boto/boto/machinelearning/ |
| layer1.py | 528 + `sgd.l1RegularizationAmount` - Coefficient regularization L1 norm. It
536 + `sgd.l2RegularizationAmount` - Coefficient regularization L2 norm. It
[all...] |
| /external/opencv3/modules/video/include/opencv2/video/ |
| tracking.hpp | 418 attachment and the regularization terms. In theory, it should have a small value in order
|
| /external/opencv3/modules/ml/include/opencv2/ |
| ml.hpp | [all...] |
