Recently, logical analysis of data (LAD) using a classifier based on a linear combination of patterns has been introduced, providing high classification accuracy and pattern-based interpretability on classification results. However, it is known that most of LAD-based multi-classification algorithms have conflicts between classification accuracy and computational complexity because they are base...

In this paper, a progressive learning technique for multi -class classification is proposed. This newly developed learning technique is independent of the number of class constraints and it can learn new classes while still retaining the knowledge of previous classes. Whenever a new class (non-native to the knowledge learnt thus far) is encountered, the neural network structure gets remodeled a...

A unified view on multi-class support vector machines (SVMs) is presented, covering most prominent variants including the one-vs-all approach and the algorithms proposed by Weston & Watkins, Crammer & Singer, Lee, Lin, & Wahba, and Liu & Yuan. The unification leads to a template for the quadratic training problems and new multi-class SVM formulations. Within our framework, we provide a comparat...

In this work we address the problem of multi-class classification in machine learning. In particular, we consider the coding approach which converts a multi-class problem to several binary classification problems by mapping the binary labeled space into several partitioned binary labeled spaces through binary channel codes. By modeling this learning problem as a communication channel, these cod...

We investigate strategies to improve the utterance verification performance using a 2-class pattern classification approach, including: utilizing N-best candidate scores, modifying segmentation boundaries, applying background and out-of-vocabulary filler models, incorporating contexts, and minimizing verification errors via discriminative training. A connected-digit database recorded in a noisy...

c classes are characterized by unknown probability distributions. A data sample containing labelled vectors from each of the c classes is available. The data sample is divided into test and training samples. A classifier is designed based on the training sample and evaluated with the test sample. The classifier is also evaluated based on its asymptotic properties as sample size increases. A mul...

Fold recognition based on sequence-derived features is a complex multi-class classification problem. In the current study, we comparatively assess five different classification techniques, namely multilayer perceptron and probabilistic neural networks, nearest neighbour classifiers, multi-class support vector machines and classification trees for fold recognition on a reference set of proteins ...

The Winnow family of learning algorithms can cope well with large numbers of features and is tolerant to variations in document length, which makes it suitable for classifying large collections of large documents, like patent applications. Both the large size of the documents and the large number of available training documents for each class make this classification task qualitatively differen...

The protein structural class is considered as a multi-class classification. The feature of protein structure was extracted from the protein convex hull which was occupied from the geometrical approximation of protein structure. This multi-class classification is solved by Multi-NSVR(-S), which is constructed by our algorithm-NSVR. To overcome the difficulty of class imbalance in the data, the m...

We present specializing, a method for combining classifiers for multi-class classification. Specializing trains one specialist classifier per class and utilizes each specialist to distinguish that class from all others in a one-versus-all manner. It then supplements the specialist classifiers with a catch-all classifier that performs multi-class classification across all classes. We refer to th...