export interface OnlineKernelELMOptions {
    kernel: {
        type: 'rbf' | 'polynomial' | 'linear';
        gamma?: number;
        degree?: number;
        coef0?: number;
    };
    ridgeLambda?: number;
    categories: string[];
    windowSize?: number;
    decayFactor?: number;
    landmarkStrategy?: 'uniform' | 'random' | 'adaptive';
    maxLandmarks?: number;
}
export interface OnlineKernelELMResult {
    label: string;
    prob: number;
}
/**
 * Online Kernel ELM for real-time learning from streaming data
 * Features:
 * - Incremental kernel matrix updates
 * - Sliding window with forgetting
 * - Adaptive landmark selection
 * - Real-time prediction
 */
export declare class OnlineKernelELM {
    private kernelType;
    private kernelParams;
    private categories;
    private ridgeLambda;
    private windowSize;
    private decayFactor;
    private maxLandmarks;
    private landmarks;
    private landmarkIndices;
    private samples;
    private labels;
    private sampleWeights;
    private onlineRidge;
    private kernelMatrix;
    private kernelMatrixInv;
    private trained;
    constructor(options: OnlineKernelELMOptions);
    /**
     * Initial training with batch data
     */
    fit(X: number[][], y: number[] | number[][]): void;
    /**
     * Incremental update with new sample
     */
    update(x: number[], y: number | number[]): void;
    /**
     * Predict with online model
     */
    predict(x: number[] | number[][], topK?: number): OnlineKernelELMResult[];
    /**
     * Select landmarks from data
     */
    private _selectLandmarks;
    /**
     * Compute kernel features for a sample
     */
    private _computeKernelFeatures;
    /**
     * Compute kernel between two vectors
     */
    private _kernel;
    private _dot;
    private _squaredDistance;
    private _computeKernelMatrix;
    private _updateLandmarksAdaptive;
    private _toOneHot;
    private _softmax;
    private _argmax;
    get landmarkStrategy(): 'uniform' | 'random' | 'adaptive';
}
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