In this work, we developed classifiers to distinguish between four ovarian tumor types using Bayesian least squares support vector machines (LS-SVMs) and kernel logistic regression...
Ben Van Calster, Dirk Timmerman, Antonia C. Testa,...
In this paper we propose an extension of sequence kernels to the case where the symbols that define the sequences have multiple representations. This configuration occurs in natura...
We show how to improve a state-of-the-art neural network language model that converts the previous "context" words into feature vectors and combines these feature vectors...
Abstract. This paper introduces a new concept to the processing of graph structured information using self organising map framework. Previous approaches to this problem were limite...
Markus Hagenbuchner, Alessandro Sperduti, Ah Chung...
Abstract. An architecture for achieving word recognition and incremental learning of new words in a language processing system is presented. The architecture is based on neural ass...
This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental ...
Abstract. This paper discusses a machine learning approach for binary classification problems which satisfies the specific requirements of safety-related applications. The approach...
An initialization mechanism is presented for Kohonen neural network implemented in CMOS technology. Proper selection of initial values of neurons' weights has a large influenc...
This study examines a selection of off-the-shelf forecasting and forecast combination algorithms with a focus on assessing their practical relevance by drawing conclusions for non-...
This paper describes a new topological map dedicated to clustering under instance-level constraints. In general, traditional clustering is used in an unsupervised manner. However,...