We present a novel method employing a hierarchical domain ontology structure to select features representing documents. All raw words in the training documents are mapped to concepts in a domain ontology. Based on these concepts, a concept hierarchy is established for the training document space, using is-a relationships defined in the domain ontology. An optimum concept set may be obtained by ...

Training an artificial neural network (ANN) is an optimization task since it is desired to find optimal neurons‘ weight of a neural network in an iterative training process. Traditional training algorithms have some drawbacks such as local minima and its slowness. Therefore, evolutionary algorithms are utilized to train neural networks to overcome these issues. This research tackles the ANN tra...

This paper proposes a framework for constructing and training radial basis function (RBF) neural networks. The proposed growing radial basis function (GRBF) network begins with a small number of prototypes, which determine the locations of radial basis functions. In the process of training, the GRBF network grows by splitting one of the prototypes at each growing cycle. Two splitting criteria a...

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic s...

This paper develops algorithms to train support vector machines when training data are distributed across different nodes, and their communication to a centralized processing unit is prohibited due to, for example, communication complexity, scalability, or privacy reasons. To accomplish this goal, the centralized linear SVM problem is cast as a set of decentralized convex optimization subproble...

As the number of applications that use machine learning algorithms increases, the need for labeled data useful for training such algorithms intensifies. Getting labels typically involves employing humans to do the annotation, which directly translates to training and working costs. Crowdsourcing platforms have made labeling cheaper and faster, but they still involve significant costs, especiall...

AdaBoost.M2 is a boosting algorithm designed for multiclass problems with weak base classifiers. The algorithm is designed to minimize a very loose bound on the training error. We propose two alternative boosting algorithms which also minimize bounds on performance measures. These performance measures are not as strongly connected to the expected error as the training error, but the derived bou...

One important subtask of Referring Expression Generation (REG) algorithms is to select the attributes in a definite description for a given object. In this paper, we study how much training data is required for algorithms to do this properly. We compare two REG algorithms in terms of their performance: the classic Incremental Algorithm and the more recent Graph algorithm. Both rely on a notion ...

The generalization capability and training time of conventional neural networks depend on their architecture. In conventional neural networks, we have to define the architecture prior to training but in constructive neural network (CoNN) algorithms the network architecture is constructed during the training process. This paper presents an overview of CoNN algorithms that constructing feedforwar...