We have developed two quantum classifier models for the ttH classification problem, both of which fall into category hybrid quantumclassical algorithms Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof concept that Machine Learning (QML) methods can similar or better performance, in specific cases low number training samples, respect to con...

We present CartoonX (Cartoon Explanation), a novel model-agnostic explanation method tailored towards image classifiers and based on the rate-distortion (RDE) framework. Natural images are roughly piece-wise smooth signals—also called cartoon-like images—and tend to be sparse in wavelet domain. is first exploit this by requiring its explanations domain, thus extracting relevant part of an inste...

One approach in classification tasks is to use machine learning techniques to derive classifiers using learning instances. The cooperation of several base classifiers as a decision committee has succeeded to reduce classification error. The main current decision committee learning approaches boosting and bagging use resampling with the training set and they can be used with different machine le...

This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to ...

Hierarchical classifiers are usually defined as methods of classifying inputs into defined output categories. The classification occurs first on a low-level with highly specific pieces of input data. The classifications of the individual pieces of data are then combined systematically and classified on a higher level iteratively until one output is produced. This final output is the overall cla...

In this paper we propose a novel approach for ensemble construction based on the use of linear projections to achieve both accuracy and diversity of individual classifiers. The proposed approach uses the philosophy of boosting, putting more effort on difficult instances, but instead of learning the classifier on a biased distribution of the training set it uses misclassified instances to find a...