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SHOGUN
4.1.0
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Multiple Kernel Learning.
A support vector machine based method for use with multiple kernels. In Multiple Kernel Learning (MKL) in addition to the SVM \(\bf\alpha\) and bias term \(b\) the kernel weights \(\bf\beta\) are estimated in training. The resulting kernel method can be stated as
\[ f({\bf x})=\sum_{i=0}^{N-1} \alpha_i \sum_{j=0}^M \beta_j k_j({\bf x}, {\bf x_i})+b . \]
where \(N\) is the number of training examples \(\alpha_i\) are the weights assigned to each training example \(\beta_j\) are the weights assigned to each sub-kernel \(k_j(x,x')\) are sub-kernels and \(b\) the bias.
Kernels have to be chosen a-priori. In MKL \(\alpha_i,\;\beta\) and bias are determined by solving the following optimization program
\begin{eqnarray*} \mbox{min} && \gamma-\sum_{i=1}^N\alpha_i\\ \mbox{w.r.t.} && \gamma\in R, \alpha\in R^N \nonumber\\ \mbox{s.t.} && {\bf 0}\leq\alpha\leq{\bf 1}C,\;\;\sum_{i=1}^N \alpha_i y_i=0 \nonumber\\ && \frac{1}{2}\sum_{i,j=1}^N \alpha_i \alpha_j y_i y_j k_k({\bf x}_i,{\bf x}_j)\leq \gamma,\;\; \forall k=1,\ldots,K\nonumber\\ \end{eqnarray*}
here C is a pre-specified regularization parameter.
Within shogun this optimization problem is solved using semi-infinite programming. For 1-norm MKL using one of the two approaches described in
Soeren Sonnenburg, Gunnar Raetsch, Christin Schaefer, and Bernhard Schoelkopf. Large Scale Multiple Kernel Learning. Journal of Machine Learning Research, 7:1531-1565, July 2006.
The first approach (also called the wrapper algorithm) wraps around a single kernel SVMs, alternatingly solving for \(\alpha\) and \(\beta\). It is using a traditional SVM to generate new violated constraints and thus requires a single kernel SVM and any of the SVMs contained in shogun can be used. In the MKL step either a linear program is solved via glpk or cplex or analytically or a newton (for norms>1) step is performed.
The second much faster but also more memory demanding approach performing interleaved optimization, is integrated into the chunking-based SVMlight.
In addition sparsity of MKL can be controlled by the choice of the \(L_p\)-norm regularizing \(\beta\) as described in
Marius Kloft, Ulf Brefeld, Soeren Sonnenburg, and Alexander Zien. Efficient and accurate lp-norm multiple kernel learning. In Advances in Neural Information Processing Systems 21. MIT Press, Cambridge, MA, 2009.
An alternative way to control the sparsity is the elastic-net regularization, which can be formulated into the following optimization problem:
\begin{eqnarray*} \mbox{min} && C\sum_{i=1}^N\ell\left(\sum_{k=1}^Kf_k(x_i)+b,y_i\right)+(1-\lambda)\left(\sum_{k=1}^K\|f_k\|_{\mathcal{H}_k}\right)^2+\lambda\sum_{k=1}^K\|f_k\|_{\mathcal{H}_k}^2\\ \mbox{w.r.t.} && f_1\in\mathcal{H}_1,f_2\in\mathcal{H}_2,\ldots,f_K\in\mathcal{H}_K,\,b\in R \nonumber\\ \end{eqnarray*}
where \(\ell\) is a loss function. Here \(\lambda\) controls the trade-off between the two regularization terms. \(\lambda=0\) corresponds to \(L_1\)-MKL, whereas \(\lambda=1\) corresponds to the uniform-weighted combination of kernels ( \(L_\infty\)-MKL). This approach was studied by Shawe-Taylor (2008) "Kernel Learning for Novelty Detection" (NIPS MKL Workshop 2008) and Tomioka & Suzuki (2009) "Sparsity-accuracy trade-off in MKL" (NIPS MKL Workshop 2009).
静态 Public 成员函数 | |
| static bool | perform_mkl_step_helper (CMKL *mkl, const float64_t *sumw, const float64_t suma) |
| static void * | apply_helper (void *p) |
Public 属性 | |
| SGIO * | io |
| Parallel * | parallel |
| Version * | version |
| Parameter * | m_parameters |
| Parameter * | m_model_selection_parameters |
| Parameter * | m_gradient_parameters |
| uint32_t | m_hash |
Protected 成员函数 | |
| virtual bool | train_machine (CFeatures *data=NULL) |
| virtual void | init_training ()=0 |
| void | perform_mkl_step (float64_t *beta, float64_t *old_beta, int num_kernels, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *lin, float64_t *sumw, int32_t &inner_iters) |
| float64_t | compute_optimal_betas_via_cplex (float64_t *beta, const float64_t *old_beta, int32_t num_kernels, const float64_t *sumw, float64_t suma, int32_t &inner_iters) |
| float64_t | compute_optimal_betas_via_glpk (float64_t *beta, const float64_t *old_beta, int num_kernels, const float64_t *sumw, float64_t suma, int32_t &inner_iters) |
| float64_t | compute_optimal_betas_elasticnet (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective) |
| void | elasticnet_transform (float64_t *beta, float64_t lmd, int32_t len) |
| void | elasticnet_dual (float64_t *ff, float64_t *gg, float64_t *hh, const float64_t &del, const float64_t *nm, int32_t len, const float64_t &lambda) |
| float64_t | compute_optimal_betas_directly (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective) |
| float64_t | compute_optimal_betas_block_norm (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective) |
| float64_t | compute_optimal_betas_newton (float64_t *beta, const float64_t *old_beta, int32_t num_kernels, const float64_t *sumw, float64_t suma, float64_t mkl_objective) |
| virtual bool | converged () |
| void | init_solver () |
| bool | init_cplex () |
| void | set_qnorm_constraints (float64_t *beta, int32_t num_kernels) |
| bool | cleanup_cplex () |
| bool | init_glpk () |
| bool | cleanup_glpk () |
| bool | check_glp_status (glp_prob *lp) |
| virtual float64_t * | get_linear_term_array () |
| SGVector< float64_t > | apply_get_outputs (CFeatures *data) |
| virtual void | store_model_features () |
| virtual bool | is_label_valid (CLabels *lab) const |
| virtual bool | train_require_labels () const |
| virtual void | load_serializable_pre () throw (ShogunException) |
| virtual void | load_serializable_post () throw (ShogunException) |
| virtual void | save_serializable_pre () throw (ShogunException) |
| virtual void | save_serializable_post () throw (ShogunException) |
Protected 属性 | |
| CSVM * | svm |
| float64_t | C_mkl |
| float64_t | mkl_norm |
| float64_t | ent_lambda |
| float64_t | mkl_block_norm |
| float64_t * | beta_local |
| int32_t | mkl_iterations |
| float64_t | mkl_epsilon |
| bool | interleaved_optimization |
| float64_t * | W |
| float64_t | w_gap |
| float64_t | rho |
| CTime | training_time_clock |
| CPXENVptr | env |
| CPXLPptr | lp_cplex |
| glp_prob * | lp_glpk |
| glp_smcp * | lp_glpk_parm |
| bool | lp_initialized |
| SGVector< float64_t > | m_linear_term |
| bool | svm_loaded |
| float64_t | epsilon |
| float64_t | tube_epsilon |
| float64_t | nu |
| float64_t | C1 |
| float64_t | C2 |
| float64_t | objective |
| int32_t | qpsize |
| bool | use_shrinking |
| bool(* | callback )(CMKL *mkl, const float64_t *sumw, const float64_t suma) |
| CMKL * | mkl |
| CKernel * | kernel |
| CCustomKernel * | m_custom_kernel |
| CKernel * | m_kernel_backup |
| bool | use_batch_computation |
| bool | use_linadd |
| bool | use_bias |
| float64_t | m_bias |
| SGVector< float64_t > | m_alpha |
| SGVector< int32_t > | m_svs |
| float64_t | m_max_train_time |
| CLabels * | m_labels |
| ESolverType | m_solver_type |
| bool | m_store_model_features |
| bool | m_data_locked |
apply machine to data if data is not specified apply to the current features
| data | (test)data to be classified |
在文件 Machine.cpp 第 152 行定义.
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virtualinherited |
apply kernel machine to data for binary classification task
| data | (test)data to be classified |
重载 CMachine .
被 CDomainAdaptationSVM 重载.
在文件 KernelMachine.cpp 第 248 行定义.
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staticinherited |
apply example helper, used in threads
| p | params of the thread |
在文件 KernelMachine.cpp 第 424 行定义.
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apply machine to data in means of latent problem
被 CLinearLatentMachine 重载.
在文件 Machine.cpp 第 232 行定义.
Applies a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is predicted |
在文件 Machine.cpp 第 187 行定义.
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virtualinherited |
Applies a locked machine on a set of indices. Error if machine is not locked. Binary case
| indices | index vector (of locked features) that is predicted |
重载 CMachine .
在文件 KernelMachine.cpp 第 518 行定义.
Applies a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is predicted |
在文件 KernelMachine.cpp 第 531 行定义.
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virtualinherited |
applies a locked machine on a set of indices for latent problems
在文件 Machine.cpp 第 266 行定义.
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virtualinherited |
applies a locked machine on a set of indices for multiclass problems
在文件 Machine.cpp 第 252 行定义.
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virtualinherited |
Applies a locked machine on a set of indices. Error if machine is not locked. Binary case
| indices | index vector (of locked features) that is predicted |
重载 CMachine .
在文件 KernelMachine.cpp 第 524 行定义.
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virtualinherited |
applies a locked machine on a set of indices for structured problems
在文件 Machine.cpp 第 259 行定义.
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virtualinherited |
apply machine to data in means of multiclass classification problem
被 CNeuralNetwork, CCHAIDTree, CCARTree, CGaussianProcessClassification, CMulticlassMachine, CKNN, CC45ClassifierTree, CID3ClassifierTree, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CQDA, CRelaxedTree , 以及 CBaggingMachine 重载.
在文件 Machine.cpp 第 220 行定义.
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virtualinherited |
apply kernel machine to one example
| num | which example to apply to |
重载 CMachine .
在文件 KernelMachine.cpp 第 405 行定义.
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virtualinherited |
apply kernel machine to data for regression task
| data | (test)data to be classified |
重载 CMachine .
在文件 KernelMachine.cpp 第 242 行定义.
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virtualinherited |
apply machine to data in means of SO classification problem
被 CLinearStructuredOutputMachine 重载.
在文件 Machine.cpp 第 226 行定义.
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Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
| dict | dictionary of parameters to be built. |
在文件 SGObject.cpp 第 597 行定义.
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protected |
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Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
在文件 SGObject.cpp 第 714 行定义.
| float64_t compute_elasticnet_dual_objective | ( | ) |
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| float64_t compute_mkl_primal_objective | ( | ) |
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protected |
given the alphas, compute the corresponding optimal betas
| beta | new betas (kernel weights) |
| old_beta | old betas (previous kernel weights) |
| num_kernels | number of kernels |
| sumw | 1/2*alpha'*K_j*alpha for each kernel j |
| suma | (sum over alphas) |
| mkl_objective | the current mkl objective |
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protected |
given the alphas, compute the corresponding optimal betas
| beta | new betas (kernel weights) |
| old_beta | old betas (previous kernel weights) |
| num_kernels | number of kernels |
| sumw | 1/2*alpha'*K_j*alpha for each kernel j |
| suma | (sum over alphas) |
| mkl_objective | the current mkl objective |
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protected |
given the alphas, compute the corresponding optimal betas
| beta | new betas (kernel weights) |
| old_beta | old betas (previous kernel weights) |
| num_kernels | number of kernels |
| sumw | 1/2*alpha'*K_j*alpha for each kernel j |
| suma | (sum over alphas) |
| mkl_objective | the current mkl objective |
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protected |
given the alphas, compute the corresponding optimal betas
| beta | new betas (kernel weights) |
| old_beta | old betas (previous kernel weights) |
| num_kernels | number of kernels |
| sumw | 1/2*alpha'*K_j*alpha for each kernel j |
| suma | (sum over alphas) |
| mkl_objective | the current mkl objective |
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protected |
given the alphas, compute the corresponding optimal betas using a lp for 1-norm mkl, a qcqp for 2-norm mkl and an iterated qcqp for general q-norm mkl.
| beta | new betas (kernel weights) |
| old_beta | old betas (previous kernel weights) |
| num_kernels | number of kernels |
| sumw | 1/2*alpha'*K_j*alpha for each kernel j |
| suma | (sum over alphas) |
| inner_iters | number of internal iterations (for statistics) |
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protected |
given the alphas, compute the corresponding optimal betas using a lp for 1-norm mkl
| beta | new betas (kernel weights) |
| old_beta | old betas (previous kernel weights) |
| num_kernels | number of kernels |
| sumw | 1/2*alpha'*K_j*alpha for each kernel j |
| suma | (sum over alphas) |
| inner_iters | number of internal iterations (for statistics) |
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pure virtual |
compute beta independent term from objective, e.g., in 2-class MKL sum_i alpha_i etc
在 CMKLRegression, CMKLOneClass , 以及 CMKLClassification 内被实现.
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create new model
| num | number of alphas and support vectors in new model |
在文件 KernelMachine.cpp 第 194 行定义.
Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called.
Computes kernel matrix to speed up train/apply calls
| labs | labels used for locking |
| features | features used for locking |
重载 CMachine .
在文件 KernelMachine.cpp 第 623 行定义.
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virtualinherited |
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virtualinherited |
A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 198 行定义.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
| other | object to compare with |
| accuracy | accuracy to use for comparison (optional) |
| tolerant | allows linient check on float equality (within accuracy) |
在文件 SGObject.cpp 第 618 行定义.
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在文件 KernelMachine.cpp 第 189 行定义.
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check if batch computation is enabled
在文件 KernelMachine.cpp 第 99 行定义.
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get classifier type
被 CLaRank, CSVMLight, CDualLibQPBMSOSVM, CNeuralNetwork, CCCSOSVM, CLeastAngleRegression, CLDA, CKernelRidgeRegression, CLibLinearMTL, CBaggingMachine, CLibLinear, CGaussianProcessClassification, CKMeans, CLibSVR, CQDA, CGaussianNaiveBayes, CSVRLight, CMCLDA, CLinearRidgeRegression, CKNN, CScatterSVM, CGaussianProcessRegression, CSGDQN, CSVMSGD, CSVMOcas, COnlineSVMSGD, CLeastSquaresRegression, CMKLRegression, CDomainAdaptationSVMLinear, CMKLMulticlass, CWDSVMOcas, CHierarchical, CMKLOneClass, CLibSVM, CStochasticSOSVM, CMKLClassification, CDomainAdaptationSVM, CLPBoost, CPerceptron, CAveragedPerceptron, CFWSOSVM, CNewtonSVM, CLPM, CGMNPSVM, CSVMLightOneClass, CSVMLin, CMulticlassLibSVM, CLibSVMOneClass, CMPDSVM, CGPBTSVM, CGNPPSVM , 以及 CCPLEXSVM 重载.
在文件 Machine.cpp 第 92 行定义.
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| bool get_interleaved_optimization_enabled | ( | ) |
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returns type of problem machine solves
被 CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree , 以及 CBaseMulticlassMachine 重载.
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| float64_t get_mkl_epsilon | ( | ) |
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在文件 SGObject.cpp 第 498 行定义.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
| param_name | name of the parameter |
在文件 SGObject.cpp 第 522 行定义.
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Returns index of model selection parameter with provided index
| param_name | name of model selection parameter |
在文件 SGObject.cpp 第 535 行定义.
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get support vector at given index
| idx | index of support vector |
在文件 KernelMachine.cpp 第 134 行定义.
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inherited |
在文件 KernelMachine.cpp 第 184 行定义.
| CSVM* get_svm | ( | ) |
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protectedpure virtual |
check run before starting training (to e.g. check if labeling is two-class labeling in classification case
在 CMKLRegression, CMKLOneClass , 以及 CMKLClassification 内被实现.
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If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
| generic | set to the type of the generic if returning TRUE |
在文件 SGObject.cpp 第 296 行定义.
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protectedvirtualinherited |
check whether the labels is valid.
Subclasses can override this to implement their check of label types.
| lab | the labels being checked, guaranteed to be non-NULL |
被 CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression , 以及 CBaseMulticlassMachine 重载.
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Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
| file | where to load from |
| prefix | prefix for members |
在文件 SGObject.cpp 第 369 行定义.
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Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
| ShogunException | will be thrown if an error occurs. |
被 CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.
在文件 SGObject.cpp 第 426 行定义.
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Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
| ShogunException | will be thrown if an error occurs. |
被 CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 421 行定义.
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problem type
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virtualinherited |
在文件 SGObject.cpp 第 262 行定义.
perform single mkl iteration
given sum of alphas, objectives for current alphas for each kernel and current kernel weighting compute the corresponding optimal kernel weighting (all via get/set_subkernel_weights in CCombinedKernel)
| sumw | vector of 1/2*alpha'*K_j*alpha for each kernel j |
| suma | scalar sum_i alpha_i etc. |
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protected |
perform single mkl iteration
given the alphas, compute the corresponding optimal betas
| beta | new betas (kernel weights) |
| old_beta | old betas (previous kernel weights) |
| num_kernels | number of kernels |
| label | (from svmlight label) |
| active2dnum | (from svmlight active2dnum) |
| a | (from svmlight alphas) |
| lin | (from svmlight linear components) |
| sumw | 1/2*alpha'*K_j*alpha for each kernel j |
| inner_iters | number of required internal iterations |
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prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 474 行定义.
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Save this object to file.
| file | where to save the object; will be closed during returning if PREFIX is an empty string. |
| prefix | prefix for members |
在文件 SGObject.cpp 第 314 行定义.
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Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
| ShogunException | will be thrown if an error occurs. |
被 CKernel 重载.
在文件 SGObject.cpp 第 436 行定义.
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Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
| ShogunException | will be thrown if an error occurs. |
被 CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 431 行定义.
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set alpha at given index to given value
| idx | index of alpha vector |
| val | new value of alpha vector |
在文件 KernelMachine.cpp 第 159 行定义.
set alphas to given values
| alphas | float vector with all alphas to set |
在文件 KernelMachine.cpp 第 174 行定义.
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set batch computation enabled
| enable | if batch computation shall be enabled |
在文件 KernelMachine.cpp 第 94 行定义.
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| void set_constraint_generator | ( | CSVM * | s | ) |
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| void set_elasticnet_lambda | ( | float64_t | elasticnet_lambda | ) |
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在文件 SGObject.cpp 第 41 行定义.
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在文件 SGObject.cpp 第 46 行定义.
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在文件 SGObject.cpp 第 51 行定义.
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在文件 SGObject.cpp 第 56 行定义.
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在文件 SGObject.cpp 第 61 行定义.
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在文件 SGObject.cpp 第 66 行定义.
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在文件 SGObject.cpp 第 71 行定义.
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在文件 SGObject.cpp 第 76 行定义.
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在文件 SGObject.cpp 第 81 行定义.
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在文件 SGObject.cpp 第 86 行定义.
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在文件 SGObject.cpp 第 91 行定义.
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在文件 SGObject.cpp 第 96 行定义.
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在文件 SGObject.cpp 第 101 行定义.
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在文件 SGObject.cpp 第 106 行定义.
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在文件 SGObject.cpp 第 111 行定义.
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set generic type to T
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| void set_interleaved_optimization_enabled | ( | bool | enable | ) |
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set labels
| lab | labels |
被 CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree , 以及 CMulticlassMachine 重载.
在文件 Machine.cpp 第 65 行定义.
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| void set_mkl_block_norm | ( | float64_t | q | ) |
| void set_mkl_epsilon | ( | float64_t | eps | ) |
| void set_mkl_norm | ( | float64_t | norm | ) |
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Setter for store-model-features-after-training flag
| store_model | whether model should be stored after training |
在文件 Machine.cpp 第 107 行定义.
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set support vector at given index to given value
| idx | index of support vector |
| val | new value of support vector |
在文件 KernelMachine.cpp 第 149 行定义.
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set support vectors to given values
| svs | integer vector with all support vectors indexes to set |
在文件 KernelMachine.cpp 第 179 行定义.
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inherited |
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virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
被 CGaussianKernel 重载.
在文件 SGObject.cpp 第 192 行定义.
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protectedvirtualinherited |
Stores feature data of the SV indices and sets it to the lhs of the underlying kernel. Then, all SV indices are set to identity.
May be overwritten by subclasses in case the model should be stored differently.
重载 CMachine .
在文件 KernelMachine.cpp 第 453 行定义.
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virtualinherited |
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virtualinherited |
train machine
| data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training. |
被 CRelaxedTree, CAutoencoder, CSGDQN , 以及 COnlineSVMSGD 重载.
在文件 Machine.cpp 第 39 行定义.
Trains a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is used for training |
重载 CMachine .
在文件 KernelMachine.cpp 第 482 行定义.
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protectedvirtual |
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protectedvirtualinherited |
returns whether machine require labels for training
被 COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree , 以及 CLibSVMOneClass 重载.
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inherited |
unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 303 行定义.
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virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 248 行定义.
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protected |
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protected |
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protected |
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inherited |
io
在文件 SGObject.h 第 369 行定义.
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protectedinherited |
kernel
在文件 KernelMachine.h 第 311 行定义.
coefficients alpha
在文件 KernelMachine.h 第 332 行定义.
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protectedinherited |
bias term b
在文件 KernelMachine.h 第 329 行定义.
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protectedinherited |
is filled with pre-computed custom kernel on data lock
在文件 KernelMachine.h 第 314 行定义.
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inherited |
parameters wrt which we can compute gradients
在文件 SGObject.h 第 384 行定义.
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inherited |
Hash of parameter values
在文件 SGObject.h 第 387 行定义.
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protectedinherited |
old kernel is stored here on data lock
在文件 KernelMachine.h 第 317 行定义.
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inherited |
model selection parameters
在文件 SGObject.h 第 381 行定义.
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inherited |
parameters
在文件 SGObject.h 第 378 行定义.
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protectedinherited |
|
protectedinherited |
|
protectedinherited |
array of ``support vectors'' (indices of feature objects)
在文件 KernelMachine.h 第 335 行定义.
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protectedinherited |
|
protected |
|
inherited |
parallel
在文件 SGObject.h 第 372 行定义.
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protected |
measures training time for use with get_max_train_time()
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protectedinherited |
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protectedinherited |
if batch computation is enabled
在文件 KernelMachine.h 第 320 行定义.
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protectedinherited |
if bias shall be used
在文件 KernelMachine.h 第 326 行定义.
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protectedinherited |
if linadd is enabled
在文件 KernelMachine.h 第 323 行定义.
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inherited |
version
在文件 SGObject.h 第 375 行定义.
