function(sample, labels) {
		labels = multilabelutils.normalizeOutputLabels(labels);
		for (var l in labels) {
			var positiveLabel = labels[l];
			this.makeSureClassifierExists(positiveLabel);
			this.mapClassnameToClassifier[positiveLabel].trainOnline(sample, 1);
		}
		for (var negativeLabel in this.mapClassnameToClassifier) {
			if (labels.indexOf(negativeLabel)<0)
				this.mapClassnameToClassifier[negativeLabel].trainOnline(sample, 0);
		}
	}
function(sample, labels) {
		labels = multilabelutils.normalizeOutputLabels(labels);
		for (var l in labels) {
			var positiveLabel = labels[l];
			this.makeSureClassifierExists(positiveLabel);
			this.mapClassnameToClassifier[positiveLabel].trainOnline(sample, 1);
		}
		for (var negativeLabel in this.mapClassnameToClassifier) {
			if (labels.indexOf(negativeLabel)<0)
				this.mapClassnameToClassifier[negativeLabel].trainOnline(sample, 0);
		}
	}
function ( sample , labels ) { labels = multilabelutils . normalizeOutputLabels ( labels ) ; for ( var l in labels ) { var positiveLabel = labels [ l ] ; this . makeSureClassifierExists ( positiveLabel ) ; this . mapClassnameToClassifier [ positiveLabel ] . trainOnline ( sample , 1 ) ; } for ( var negativeLabel in this . mapClassnameToClassifier ) { if ( labels . indexOf ( negativeLabel ) < 0 ) this . mapClassnameToClassifier [ negativeLabel ] . trainOnline ( sample , 0 ) ; } }
Tell the classifier that the given sample belongs to the given labels.

@param sample
a document.
@param labels
an object whose KEYS are labels, or an array whose VALUES are labels.
Tell the classifier that the given sample belongs to the given labels .