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Tool use constitutes a range of complex behaviors that generally require a sophisticated level of cognition, and is only found in higher mammals and a number of avian species. In this paper, we will examine how different strategies for using a tool emerge during the simulated evolution of a two degree-of-freedom articulated limb in a reaching task environment. The limb is controlled by recurrent neural...
We propose a conjugate descent procedure for the second order SMO algorithm that leads to a substantial decrease in the number of iterations required for SMO to converge to a given precision with a modest increase in the iteration cost.
This study brings together systematised views of two related areas: data editing for the nearest neighbour classifier and adaptive learning in the presence of concept drift. The growing number of studies in the intersection of these areas warrants a closer look. We revise and update the taxonomies of the two areas proposed in the literature and argue that they are not sufficiently discriminative with...
The recent success of representation learning is built upon the learning of relevant features, in particular from unlabelled data available in different domains. This raises the question of how to transfer and reuse such knowledge effectively so that the learning of a new task can be made easier or be improved. This poses a difficult challenge for the area of transfer learning where there is no label...
A key requirement for supervised learning is the availability of sufficient amount of labeled data to build an accurate prediction model. However, obtaining labeled data can be manually tedious and expensive. This paper examines the use of crowdsourcing technology to acquire labeled examples for classifying network data. Unfortunately, creating human intelligence tasks (HITs) to enable crowdsourcing...
One identical weighting scheme for each sample of one cluster is often employed in the traditional sample weighting k-means clustering. However, this paper proposes a novel sample weighting k-means clustering algorithm based on angles information(SWKMA). In this presented SWKMA, firstly, samples of one cluster is divided into two types according to angles information, and secondly, different weighting...
Deep learning has recently gained popularity in many machine learning applications, but a theoretical grounding for the strengths, weaknesses, and implicit biases of various deep learning methods is still a work in progress. Here, we analyze the role of spatial locality in Deep Belief Networks (DBN) and show that spatially local information is lost through diffusion as the network becomes deeper....
A microblog recommendation method based on tag correlation and user social relation is proposed via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm. Specifically, a tag retrieval strategy is established to add tags for unlabeled users and users with few tags, and the user-tag matrix is then built and user-tag weights are then obtained. In order to solve...
If output signals of artificial neural network classifiers are interpreted per node as class label predictors then partial knowledge encoded by the network during the learning procedure can be exploited in order to reassign which output node should represent each class label so that learning speed and final classification accuracy are improved. Our method for computing these reassignments is based...
This paper presents an angle and density-based data preprocessing method. It can be used to simultaneously identify outliers, boundary points and center points of clusters. Boundary points and outliers are generally located around the margin of densely distributed data such as a cluster. Detecting boundary points and outliers is often more interesting than detecting normal observations since they...
We develop a Partitioned Restricted Boltzmann Machine (PRBM) for classification. We demonstrate that this method provides both speed and accuracy. Specifically, because it is partitioned into smaller RBMs, all available data can be used for training, and individual RBMs can be trained in parallel. Moreover, as the number of dimensions increases, the number of partitions can be increased to significantly...
Clustering plays a basic role in many areas of data engineering, pattern recognition and image analysis. Gaussian Mixture Model (GMM) and Cross-Entropy Clustering (CEC) can approximate data of varied shapes by covering it with several clusters e.g. elliptical ones. However, it often happens that we need to extract clusters concentrated around lower dimensional non-linear manifolds. Moreover it is...
Recently proposed multiple birth support vector machine (MBSVM), which is extended from TWSVM, is an efficient algorithm for multi-class classification. MBSVM keeps the advantage of TWSVM. However, the solution of MBSVM classifier follows solving quadratic programming. Solving quadratic programming requires long time. This paper presents a granular multiple birth support vector machine based on weighted...
The application of current drift detection methods to real data streams show trends in the rate of change found by the detectors. We observe that these patterns of change vary across different data streams. We use the term stream volatility pattern to describe change rates with a distinct mean and variance. First, we propose a novel drift prediction algorithm to predict the location of future drift...
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