## Search

Now showing items 21-30 of 60

#### Early classification of time series using multi-objective optimization techniques

(2019-04-23)

In early classification of time series the objective is to build models which are able to make class-predictions for time series as accurately and as early as possible, when only a part of the series is available. It is ...

#### 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 ...

#### Mallows and generalized Mallows model for matchings

(2019-02-25)

The Mallows and Generalized Mallows Models are two of the most popular probability models for distribu- tions on permutations. In this paper, we consider both models under the Hamming distance. This models can be seen as ...

#### Aggregated outputs by linear models: An application on marine litter beaching prediction

(2019-01-01)

In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning ...

#### Evolving Gaussian Process Kernels for Translation Editing Effort Estimation

(2019)

In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, ...

#### Bayesian Optimization Approaches for Massively Multi-modal Problems

(2019)

The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been ...

#### Anatomy of the attraction basins: Breaking with the intuition

(2019)

olving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood ...

#### Sentiment analysis with genetically evolved Gaussian kernels

(2019)

Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. ...

#### Hybrid Heuristics for the Linear Ordering Problem

(2019)

The linear ordering problem (LOP) is one of the classical NP-Hard combinatorial optimization problems. Motivated by the difficulty of solving it up to optimality, in recent decades a great number of heuristic and meta-heuristic ...

#### An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization

(2019)

Bayesian Optimization has been widely used along with Gaussian Processes for solving expensive-to-evaluate black-box optimization problems. Overall, this approach has shown good results, and particularly for parameter ...