it.uniroma2.sag.kelp.learningalgorithm

Enum PassiveAggressive.Policy

• Enum Constant Detail

• HARD_PA

public static final PassiveAggressive.Policy HARD_PA
The new prediction hypothesis after a new example $$\mathbf{x}_t$$ with label $$y_t$$ is observed is:

$$argmin_{\mathbf{w}} \frac{1}{2} \left \| \mathbf{w}-\mathbf{w}_t \right \|^2$$

such that $$l(\mathbf{w};(\mathbf{x}_t,y_t))=0$$

• PA_I

public static final PassiveAggressive.Policy PA_I
The new prediction hypothesis after a new example $$\mathbf{x}_t$$ with label $$y_t$$ is observed is:

$$argmin_{\mathbf{w}} \frac{1}{2} \left \| \mathbf{w}-\mathbf{w}_t \right \|^2 + C\xi$$

such that $$l(\mathbf{w};(\mathbf{x}_t,y_t))\leq \xi$$ and $$\xi\geq 0$$

• PA_II

public static final PassiveAggressive.Policy PA_II
The new prediction hypothesis after a new example $$\mathbf{x}_t$$ with label $$y_t$$ is observed is:

$$argmin_{\mathbf{w}} \frac{1}{2} \left \| \mathbf{w}-\mathbf{w}_t \right \|^2 + C\xi^2$$

such that $$l(\mathbf{w};(\mathbf{x}_t,y_t))\leq \xi$$ and $$\xi\geq 0$$

• Method Detail

• values

public static PassiveAggressive.Policy[] values()
Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:
for (PassiveAggressive.Policy c : PassiveAggressive.Policy.values())
System.out.println(c);

Returns:
an array containing the constants of this enum type, in the order they are declared
• valueOf

public static PassiveAggressive.Policy valueOf(String name)
Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)
Parameters:
name - the name of the enum constant to be returned.
Returns:
the enum constant with the specified name
Throws:
IllegalArgumentException - if this enum type has no constant with the specified name
NullPointerException - if the argument is null