## Search

Now showing items 1-10 of 13

#### Supervised non-parametric discretization based on Kernel density estimation

(2019-12-19)

Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main ...

#### Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance

(2019-07)

The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ...

#### On-line Elastic Similarity Measures for time series

(2019-04)

The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are ...

#### On the evaluation and selection of classifier learning algorithms with crowdsourced data

(2019-02-16)

In many current problems, the actual class of the instances, the ground truth, is unavail-
able. Instead, with the intention of learning a model, the labels can be crowdsourced by
harvesting them from different annotators. ...

#### Crowd Learning with Candidate Labeling: an EM-based Solution

(2018-09-27)

Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are
asked to provide a single label for each instance, novel approaches allow annotators, in case ...

#### Are the artificially generated instances uniform in terms of difficulty?

(2018-06)

In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a test-bed to determine the performance of the algorithms at hand. In this context, a recent work on ...

#### On-Line Dynamic Time Warping for Streaming Time Series

(2017-09)

Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning ...

#### Nature-inspired approaches for distance metric learning in multivariate time series classification

(2017-07)

The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular ...

#### An efficient approximation to the K-means clustering for Massive Data

(2017-02-01)

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...

#### Nature-inspired approaches for distance metric learning in multivariate time series classification

(2017)

The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular ...