96 citations · 328 across the 30 of their papers we have counts for
11 papers · 1 filter
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl +3
Application of interpretable machine learning techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Furthermore, the…
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices
Fengzhen Tang, Haifeng Feng, Peter Tino +2
In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the…
A Survey on Neural Network Interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis +1
Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning…
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density
Abolfazl Taghribi, Kerstin Bunte, Rory Smith +4
Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performa…
Visualisation and knowledge discovery from interpretable models
Sreejita Ghosh, Peter Tino, Kerstin Bunte
Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness…
Dynamical Systems as Temporal Feature Spaces
Peter Tino
Parameterized state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the blac…
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk +1
Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case,…
A mixture of experts model for predicting persistent weather patterns
Maria Perez-Ortiz, Pedro A. Gutierrez, Peter Tino +2
Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system w…
Feature Relevance Bounds for Ordinal Regression
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl +2
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model…
Probabilistic classifiers with low rank indefinite kernels
Frank-Michael Schleif, Andrej Gisbrecht, Peter Tino
Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval.…
Learning in the Model Space for Fault Diagnosis
Huanhuan Chen, Peter Tino, Xin Yao +1
The emergence of large scaled sensor networks facilitates the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cas…