nak.classify

SVM

object SVM

Object for creating SupportVectorMachines

Linear Supertypes
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  1. class SMOTrainer[L, T] extends Trainer[L, T] with LazyLogging

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  4. def apply[L, T](data: Seq[Example[L, T]], numIterations: Int = 1000)(implicit vspace: MutableVectorField[T, Double], man: ClassTag[T]): Classifier[L, T]

    Trains an SVM using the Pegasos Algorithm.

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  10. final def getClass(): Class[_]

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  12. final def isInstanceOf[T0]: Boolean

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  13. def main(args: Array[String]): Unit

    An online optimizer for an SVM based on Pegasos: Primal Estimated sub-GrAdient SOlver for SVM Extended with Wang, Crammer, Vucetic's work for Multiclass

    An online optimizer for an SVM based on Pegasos: Primal Estimated sub-GrAdient SOlver for SVM Extended with Wang, Crammer, Vucetic's work for Multiclass

    The optimizer runs a stochastic subgradient descent on the primal objective using batches provided.

  14. final def ne(arg0: AnyRef): Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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

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

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  21. final def wait(arg0: Long): Unit

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