We address the use of neural networks (NNs) in classifying environmental parameters single-qubit dephasing channels. In particular, we investigate performance linear perceptrons and two nonlinear NN architectures. At variance with time-series-based approaches, our goal is to learn a discretized probability distribution over using tomographic data at just random instants time. consider channels ...

In classification problems, as the number of classes increases, correctly classifying a new instance into one them is assumed to be more challenging than making same decision in presence fewer classes. The essence problem that using learning algorithm on each boundary individually better several simultaneously. However, why and when it happens still not well-understood today. This work’s...

In our research paper, we have implemented Multiclass Classification using Support Vector Machine (SVM). Pen Digit Recognition of Handwritten digit dataset is used for the purpose. One vs All approach has been applied using SVM to achieve multiclass classification. The same approach with different kernels has been analysed to select the right kernel. In this paper, we have found that selection ...

We describe a new algorithmic framework for learning multiclass categorization problems. In this framework a multiclass predictor is composed of a pair of embeddings that map both instances and labels into a common space. In this space each instance is assigned the label it is nearest to. We outline and analyze an algorithm, termed Bunching, for learning the pair of embeddings from labeled data...

This paper introduces an algorithm that uses boosting to learn a distance measure for multiclass k-nearest neighbor classi cation. Given a family of distance measures as input, AdaBoost is used to learn a weighted distance measure, that is a linear combination of the input measures. The proposed method can be seen both as a novel way to learn a distance measure from data, and as a novel way to ...

We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We provide a sufficient condition for when the hybrid loss is Fisher consistent for classification. This condition depends on a measure of dominance between labels – specific...

In this paper, an importance sampling method – cross entropy method is presented to deal with solving support vector machines (SVM) problem for multiclass classification cases. Using one-against-rest (OAR) and one-against-one (OAO) approaches, several binary svm classifiers are constructed and combined to solve multiclass classification problems. For each binary SVM classifier, the cross entrop...

Recent work highlights advantages in decomposing multiclass decision problems into multiple binary problems. Several strategies have been proposed for this decomposition. The most frequently investigated are All-vs-All, One-vs-All and the Error correction output codes (ECOC). ECOC are binary words (codewords) and can be adapted to be used in classifications problems. They must, however, comply ...

Classifiers for documents are useful for many applications. Major uses for binary classifiers include spam detection and personalization of streams of news articles. Multiclass classifiers are useful for routing messages to recipients. Most classifiers for documents are designed to categorize according to subject matter. However, it is also possible to learn to categorize according to qualitati...