LinearL2SVM

class LinearL2SVM extends AnyRef

An implementation of the liblinear algorithm. Not really a GradientOptimizer.

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new LinearL2SVM(lossType: Int = 0, cost: Double = 0.1, eps: Double = 1.0E-5, bias: Double = -1, maxIterations: Int = 1000)

lossType
1 if l1 hinge loss, 2 if hinge squared
cost
The C parameter of the SVM algorithm
eps
The tolerance of the optimizer
bias
TODO: no idea what this is - Alex
maxIterations
Maximum number of iterations

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def train(xTensors: Seq[la.Tensor1], ys: Array[Int], currLabel: Int): la.DenseTensor1
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LinearL2SVM

class LinearL2SVM extends AnyRef

Instance Constructors

new LinearL2SVM(lossType: Int = 0, cost: Double = 0.1, eps: Double = 1.0E-5, bias: Double = -1, maxIterations: Int = 1000)

Value Members

final def !=(arg0: AnyRef ): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef ): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef ): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef ): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def train(xTensors: Seq[la.Tensor1], ys: Array[Int], currLabel: Int): la.DenseTensor1

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

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