Add to ORKGThe constraint classification framework captures many flavors of multiclass classification including winner-take-all multiclass classification, multilabel classification and ranking. We present a meta-algorithm for learning in this framework that learns via a single linear classifier in high dimension. We discuss distribution independent as well as margin-based generalization bounds and present empirical and theoretical evidence showing that constraint classification benefits over existing methods of multiclass classification.
Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Con...
We introduce and formalize the multilevel classification problem, in which each category can be subd...
Multi-dimensional classification (MDC) assumes heterogenous class spaces for each example, where cla...
Classification problems in machine learning involve assigning labels to various kinds of output type...
We propose a semi-supervised framework incorporating feature mapping with multiclass classification....
We show in this paper the multiclass classification problem can be implemented in the maximum margin...
We suggest a method for multi-class learning with many classes by simultaneously learning shared cha...
Many interesting multiclass problems can be cast in the general frame- work of label ranking defined...
We extend the multi-label classification setting with constraints on labels. This leads to two new m...
This paper suggests a method for multiclass learning with many classes by simultaneously learning sh...
In this paper, we present a new type of multi-class learning algorithm called a linear-max algorithm...
Abstract. We introduce and formalize the multilevel classification problem, in which each category c...
Abstract. In past papers, we have introduced Empirical Model Learn-ing (EML) as a method to enable C...
The classical framework of learning from examples is enhanced by the introduction of hard pointwise ...
We propose a new abstraction refinement procedure based on machine learning to improve the performan...
Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Con...
We introduce and formalize the multilevel classification problem, in which each category can be subd...
Multi-dimensional classification (MDC) assumes heterogenous class spaces for each example, where cla...
Classification problems in machine learning involve assigning labels to various kinds of output type...
We propose a semi-supervised framework incorporating feature mapping with multiclass classification....
We show in this paper the multiclass classification problem can be implemented in the maximum margin...
We suggest a method for multi-class learning with many classes by simultaneously learning shared cha...
Many interesting multiclass problems can be cast in the general frame- work of label ranking defined...
We extend the multi-label classification setting with constraints on labels. This leads to two new m...
This paper suggests a method for multiclass learning with many classes by simultaneously learning sh...
In this paper, we present a new type of multi-class learning algorithm called a linear-max algorithm...
Abstract. We introduce and formalize the multilevel classification problem, in which each category c...
Abstract. In past papers, we have introduced Empirical Model Learn-ing (EML) as a method to enable C...
The classical framework of learning from examples is enhanced by the introduction of hard pointwise ...
We propose a new abstraction refinement procedure based on machine learning to improve the performan...
Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Con...
We introduce and formalize the multilevel classification problem, in which each category can be subd...
Multi-dimensional classification (MDC) assumes heterogenous class spaces for each example, where cla...