Sparse Implicit online Learning with Kernels

Hierarchy (View Summary)

Constructors

Properties

Methods

Constructors

  • Parameters

    • Optionaleta: number

      Learning rate

    • Optionallambda: number

      Regularization constant

    • Optionalc: number

      Penalty imposed on point prediction violations.

    • Optionalw: number

      Buffer size

    • Optionalkernel:
          | "gaussian"
          | "polynomial"
          | { name: "gaussian"; s?: number }
          | { d?: number; name: "polynomial" }
          | (arg0: number[], arg1: number[]) => number

      Kernel name

    • Optionalloss: "hinge" | "square" | "logistic" | "graph"

      Loss type name

    Returns SILK

Properties

_a: any[]
_c: number
_eta: number
_kernel: any
_lambda: number
_loss: (f: any, k: any, y: any) => number
_rho: number
_sv: any[]
_w: number

Methods

  • Fit model.

    Parameters

    • x: number[][]

      Training data

    • y: (-1 | 1)[]

      Target values

    Returns void

  • Returns predicted values.

    Parameters

    • data: number[][]

      Sample data

    Returns (-1 | 1)[]

    Predicted values

  • Update model parameters with one data.

    Parameters

    • x: number[]

      Training data

    • y: -1 | 1

      Target value

    Returns void