Now showing items 41-60 of 100

• #### 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 ...
• #### Application of machine learning techniques to weather forecasting ﻿

(2018-10-24)
Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the ...
• #### A review on distance based time series classification ﻿

(2018-11-01)
Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and ...
• #### Theoretical and Methodological Advances in Semi-supervised Learning and the Class-Imbalance Problem ﻿

(2018-11-30)
his paper focuses on the theoretical and practical generalization of two known and challenging situations from the field of machine learning to classification problems in which the assumption of having a single binary class ...
• #### Spatiotemporal information coupling in network navigation ﻿

(2018-12)
Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...
• #### 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 ...
• #### Belief Condensation Filtering For Rssi-Based State Estimation In Indoor Localization ﻿

(2019)
Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of ...
• #### Characterising the rankings produced by combinatorial optimisation problems and finding their intersections ﻿

(2019)
The aim of this paper is to introduce the concept of intersection between combinatorial optimisation problems. We take into account that most algorithms, in their machinery, do not consider the exact objective function ...
• #### 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 ...
• #### 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 ...
• #### Data generation approaches for topic classification in multilingual spoken dialog systems ﻿

(2019)
The conception of spoken-dialog systems (SDS) usually faces the problem of extending or adapting the system to multiple languages. This implies the creation of modules specically for the new languages, which is a time ...
• #### 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 ...
• #### 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 ...
• #### 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. ...
• #### 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 ...
• #### 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 ...
• #### 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 ...
• #### K-means for massive data ﻿

(2019-04-30)
The $K$-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due to its easiness in the implementation, straightforward parallelizability and competitive computational complexity, when ...