public class EpsilonSvmRegression extends LibCSvmSolver implements RegressionLearningAlgorithm, KernelMethod
It is a Java porting of the library LIBSVM v3.17, written in C++.
Further details can be found in:
[CC Chang & CJ Lin, 2011] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011.
[Vapnik, 1998] V. Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998.
and
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
LibSvmSolver.Pair| Modifier and Type | Field and Description |
|---|---|
protected UnivariateKernelMachineRegressionFunction |
regressor
The regression function to be returned
|
active_set, active_size, alpha, alpha_status, cn, cp, doShrinking, eps, examples, G, G_bar, kernel, l, label, logIteration, p, QD, shrinkingIteration, TAU, unshrink, y| Constructor and Description |
|---|
EpsilonSvmRegression() |
EpsilonSvmRegression(Kernel kernel,
Label label,
float c,
float pReg) |
EpsilonSvmRegression(Kernel kernel,
Label label,
float cp,
float cn,
float pReg) |
| Modifier and Type | Method and Description |
|---|---|
EpsilonSvmRegression |
duplicate()
Creates a new instance of the LearningAlgorithm initialized with the same parameters
of the learningAlgorithm to be duplicated.
|
protected float[] |
get_QD()
For each example i, it return the K_ii score
|
protected float |
get_Qij(int i,
int j) |
RegressionFunction |
getPredictionFunction()
Returns the regressor learned during the training process
|
float |
getpReg() |
void |
learn(Dataset dataset)
It starts the training process exploiting the provided
dataset |
void |
reset()
Resets all the learning process, returning to the default state.
|
void |
setKernel(Kernel kernel)
Sets the kernel this
|
void |
setLabels(Label... labels) |
void |
setpReg(float pReg) |
protected void |
swap_index(int i,
int j)
Swap the info of two examples
|
be_shrunk, calculate_rho, do_shrinking, getCSvmAlpha, select_working_setgetCn, getCp, getKernel, getLabel, getLabels, info, is_free, is_lower_bound, is_upper_bound, kernel, reconstruct_gradient, setC, setCn, setCp, setLabel, setLabels, solve, swap, swap, swap, swapclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetLabels, setLabelsgetKernelprotected UnivariateKernelMachineRegressionFunction regressor
public EpsilonSvmRegression(Kernel kernel, Label label, float cp, float cn, float pReg)
public EpsilonSvmRegression()
public EpsilonSvmRegression duplicate()
LearningAlgorithmduplicate in interface LearningAlgorithmprotected float[] get_QD()
LibSvmSolverget_QD in class LibSvmSolverprotected float get_Qij(int i,
int j)
get_Qij in class LibSvmSolverpublic float getpReg()
public void setpReg(float pReg)
public RegressionFunction getPredictionFunction()
RegressionLearningAlgorithmgetPredictionFunction in interface LearningAlgorithmgetPredictionFunction in interface RegressionLearningAlgorithmpublic void learn(Dataset dataset)
LearningAlgorithmdatasetlearn in interface LearningAlgorithmdataset - the training datapublic void reset()
LearningAlgorithmreset in interface LearningAlgorithmpublic void setKernel(Kernel kernel)
KernelMethodsetKernel in interface KernelMethodkernel - the kernel to setpublic void setLabels(Label... labels)
protected void swap_index(int i,
int j)
LibSvmSolverswap_index in class LibSvmSolveri - the first example indexj - the second example indexCopyright © 2015 Semantic Analytics Group @ Uniroma2. All rights reserved.