Search results for "Machine learning."

Wireless Interference Estimation Using Machine Learning in a Robotic Force-Seeking Scenario

2019

Cyber-physical systems are systems governed by the laws of physics that are tightly controlled by computer-based algorithms and network-based sensing and actuation. Wireless communication technology is envisioned to play a primary role in conducting the information flows within such systems. A practical industrial wireless use case involving a robot manipulator control system, an integrated wireless force-torque sensor, and a remote vision-based observer is constructed and the performance of the cyber-physical system is examined. By using readings from the remote observer, an estimation system is developed using machine learning regression techniques. We demonstrate the practicality of comb…

Towards personalized screening for hepatocellular carcinoma: Still not there

2020

In patients with HCV-related cirrhosis the annual risk of hepatocellular carcinoma (HCC) is 2–4%.1 However, with the advent of highly effective and well tolerated direct-acting antivirals...

Project Selection by Constrained Fuzzy AHP

2004

The selection of a project among a set of possible alternatives is a difficult task decision makers have to face. Difficulties in selecting a project arise because of the different goals involved and because of the large number of attributes to consider. Our approach is based upon a fuzzy extension of the Analytic Hierarchy Process (AHP). This paper focuses on the constraints that have to be considered within fuzzy AHP in order to take in account all the available information. This study demonstrates that by considering all the information deriving from the constraints better results in terms of certainty and reliability can be achieved.

Visual Information Fidelity with better Vision Models and better Mutual Information Estimates

2021

Retrieving Quantum Information with Active Learning

2019

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data a…

On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes

2010

This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern Recognition (PR) applications, and have numerous applications because of their known error bounds. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a priori target classes (values) of selected neighbors to, for example, predict the target class of the tested sample. Recently, an implementation of the k-NN, named as the Locally Linear Reconstruction (LLR) [11], has been proposed. The salien…

Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms

2016

We study how different types of preference information coming from a human decision maker can be utilized in an interactive multiobjective evolutionary optimization algorithm (MOEA). The idea is to convert different types of preference information into a unified format which can then be utilized in an interactive MOEA to guide the search towards the most preferred solution(s). The format chosen here is a set of reference vectors which is used within the interactive version of the reference vector guided evolutionary algorithm (RVEA). The proposed interactive RVEA is then applied to the multiple-disk clutch brake design problem with five objectives to demonstrate the potential of the idea in…

Exploring Multi-Objective Optimization for Multi-Label Classifier Ensembles

2019

Multi-label classification deals with the task of predicting multiple class labels for a given sample. Several performance metrics are designed in the literature to measure the quality of any multi-label classification technique. In general existing multi-label classification approaches focus on optimizing only a single performance measure. The current work builds on the hypothesis that a weighted ensemble of multiple multi-label classifiers will lead to obtain improved results. The appropriate weight combinations for combining the outputs of multiple classifiers can be selected after simultaneously optimizing different multi-label classification metrics like micro F1, hamming loss, 0/1 los…

Deformable object segmentation in ultra-sound images

2013

Breast cancer is the second most common type of cancer being the leading cause of cancer death among females both in western and in economically developing countries. Medical imaging is key for early detection, diagnosis and treatment follow-up. Despite Digital Mammography (DM) remains the reference imaging modality, Ultra-Sound (US) imaging has proven to be a successful adjunct image modality for breast cancer screening, specially as a consequence of the discriminative capabilities that US offers for differentiating between solid lesions that are benign or malignant. Despite US usability,US suffers inconveniences due to its natural noise that compromises the diagnosis capabilities of radio…

Comparing Boosting and Bagging for Decision Trees of Rankings

2021

AbstractDecision tree learning is among the most popular and most traditional families of machine learning algorithms. While these techniques excel in being quite intuitive and interpretable, they also suffer from instability: small perturbations in the training data may result in big changes in the predictions. The so-called ensemble methods combine the output of multiple trees, which makes the decision more reliable and stable. They have been primarily applied to numeric prediction problems and to classification tasks. In the last years, some attempts to extend the ensemble methods to ordinal data can be found in the literature, but no concrete methodology has been provided for preference…