Instance Constructors
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new
LinearVectorClassifier(numLabels: Int, numFeatures: Int, labelToFeatures: (L) ⇒ F)
Value Members
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final
def
!=(arg0: AnyRef): Boolean
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: AnyRef): Boolean
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final
def
==(arg0: Any): Boolean
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def
accumulateObjectiveGradient(accumulator: WeightsMapAccumulator, features: la.Tensor1, gradient: la.Tensor1, weight: Double): Unit
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final
def
asInstanceOf[T0]: T0
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def
bestLabelIndex(v: L): Int
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def
classification(v: L): Classification[L]
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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val
featureSize: Int
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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val
labelSize: Int
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val
labelToFeatures: (L) ⇒ F
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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A VectorClassifier in which the score for each class is a dot-product between the observed feature vector and a vector of parameters. Examples include NaiveBayes, MultivariateLogisticRegression, LinearSVM, and many others. Counter-examples include KNearestNeighbor.