The Virtue of Complexity in Return Prediction

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Weak teachers: assisted specification of discrete choice models using ensemble learning algorithms

Timothy Michael Hillel

Discrete Choice Models (DCMs) have a distinct advantage over Machine Learning (ML) classification algorithms, in that they employ a highly interpretable linear structure. However, a key drawback of DCMs compared to ML is the need to specify the utility fun ...

2019

A novel biomechanical approach for animal behaviour recognition using accelerometers

Kamiar Aminian, Pritish Chakravarty

Data from animal-borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have b ...

2019

Machine learning for cross-gazetteer matching of natural features

Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and be ...

2020

Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning

Farhad Rachidi-Haeri, Marcos Rubinstein, Mohammad Azadifar, Amirhossein Mostajabi, Hamidreza Karami, Alireza Ghasemi

Electromagnetic Time Reversal (EMTR) has been used to locate different types of electromagnetic sources. We propose a novel technique based on the combination of EMTR and Machine Learning (ML) for source localization. We show for the first time that ML tec ...

Springer Nature2019

Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions

David Atienza Alonso, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Niloofar Momeni

High levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observabl ...

IEEE2019

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

Ursula Röthlisberger, Anatole von Lilienfeld, Nicholas John Browning

The training of molecular models of quantum mechanical properties based on statistical machine learning requires large data sets which exemplify the map from chemical structure to molecular property. Intelligent a priori selection of training examples is o ...

2017

Sentiment Classification of Tweets using Hierarchical Classification

This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them in ...

Ieee2016