Search Results for author: Peter Tino

Found 15 papers, 2 papers with code

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

no code implementations4 Jun 2022 Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte

Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of machine learning techniques.

Interpretable Machine Learning

Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices

no code implementations1 Feb 2021 Fengzhen Tang, Haifeng Feng, Peter Tino, Bailu Si, Daxiong Ji

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization.

EEG General Classification +1

A Geometric Framework for Pitch Estimation on Acoustic Musical Signals

no code implementations8 Dec 2020 Tom Goodman, Karoline van Gemst, Peter Tino

This paper presents a geometric approach to pitch estimation (PE)-an important problem in Music Information Retrieval (MIR), and a precursor to a variety of other problems in the field.

Information Retrieval Music Information Retrieval Sound Computational Geometry Audio and Speech Processing 68R01

LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density

1 code implementation17 Sep 2020 Abolfazl Taghribi, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier F. Peletier, Peter Tino

The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.

Dimensionality Reduction

Visualisation and knowledge discovery from interpretable models

no code implementations7 May 2020 Sreejita Ghosh, Peter Tino, Kerstin Bunte

In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem.

Decision Making Fairness +1

Input-to-State Representation in linear reservoirs dynamics

no code implementations24 Mar 2020 Pietro Verzelli, Cesare Alippi, Lorenzo Livi, Peter Tino

Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance.

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

no code implementations10 Dec 2019 Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tino, Barbara Hammer

In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model.


Dynamical Systems as Temporal Feature Spaces

no code implementations15 Jul 2019 Peter Tino

We quantify richness of feature representations imposed by dynamic kernels and demonstrate that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.

Time Series

Feature Relevance Bounds for Ordinal Regression

1 code implementation20 Feb 2019 Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer

The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i. e. the prediction of ordered classes.


Probabilistic Matching: Causal Inference under Measurement Errors

no code implementations13 Mar 2017 Fani Tsapeli, Peter Tino, Mirco Musolesi

The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data.

Causal Inference

A Classification Framework for Partially Observed Dynamical Systems

no code implementations7 Jul 2016 Yuan Shen, Peter Tino, Krasimira Tsaneva-Atanasova

We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space.

Classification General Classification

Probabilistic classifiers with low rank indefinite kernels

no code implementations8 Apr 2016 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.

Image Retrieval Retrieval

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