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The analysis and study of complex networks are crucial to a number of applications. Vertex centrality measures are an important analysis mechanism to uncover or rank important elements of a given network. However, these metrics have high space and time complexity, which is a severe problem in applications that typically involve large networks. We propose and study the use of neural learning algorithms...
We present a complementary ensemble selection method that utilizes a novel priority queue-based diversity measure. The method considers voting weaknesses of the current ensemble in covering the training set, and finds a classifier that can remove the highest priority weaknesses. Individual classifiers are generated using different machine learning algorithms and different parameter settings.
Online sequence learning from streaming data is one of the most challenging topics in machine learning. Neural network models represent promising candidates for sequence learning due to their ability to learn and recognize complex temporal patterns. In this paper, we present a comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial...
There has been a dramatic increase in the sharing of opinions and information across different web platforms and social media, especially online product reviews. Cloud web portals, such as getApp.com, were designed to amalgamate cloud service information and to also examine how consumers evaluate their experience of using cloud computing products. The current literature shows the growing importance...
We review the fact that several kinds of neural networks can be trained to approximate other types of discriminant functions, thereby throwing some doubt upon the utility of the No Free Lunch theorem. Using a license plate recognition database with 36 classes, we then demonstrate that multilayer perceptrons estimate posterior probabilities very poorly when the number of classes is large. A method...
Brains learn much better than computers. But why? Is there a fundamental reason behind computers being slow learners? Often slow learning is described as computational complexity. This paper discusses that complexity of algorithms is as fundamental as Gödelian incompleteness of logic. Although the Gödel's theory is well recognized, its significance for engineering and modeling of the mind has not...
Evolving intelligent system (EIS) is a machine learning algorithm, specifically designed to deal with learning from large data streams. Although the EIS research topic has attracted various contributions over the past decade, the issue of uncertainty, temporal system dynamic, and system order are relatively unexplored by existing studies. A novel EIS, namely evolving type-2 recurrent fuzzy neural...
One of the most important topics in Human-Centered Computing (HCC) is to recognise human's activities. In this paper, the technology of wireless-based activity recognition is introduced. By using wireless signals, one can achieve Non-Line-Of-Sight (NLOS) recognition without carrying any devices. Also, it is easy to deploy a wireless-based recognition system due to the ubiquity of wireless communication...
Learning from imbalanced data poses significant challenges for machine learning algorithms, as they need to deal with uneven distribution of examples in the training set. As standard classifiers will be biased towards the majority class there exist a need for specific methods than can overcome this single-class dominance. Most of works concentrated on binary problems, where majority and minority class...
Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information among them can improve the performance of MKKM. To illustrate this idea, we propose a diversity-induced MKKM algorithm with extreme learning machine (ELM)-based feature extracting method...
The imputation of partially missing multivariate time series data is critical for its correct analysis. The biggest problems in time series data are consecutively missing values that would result in serious information loss if simply dropped from the dataset. To address this problem, we adapt the k-Nearest Neighbors algorithm in a novel way for multivariate time series imputation. The algorithm employs...
Distance metric learning (DML) is an effective similarity learning tool to learn a distance function from examples to enhance the model performance in applications of classification, regression, and ranking, etc. Most DML algorithms need to learn a Mahalanobis matrix, a positive semidefinite matrix that scales quadratically with the number of dimensions of input data. This brings huge computational...
The aim of classification in machine learning is to utilize knowledge gained from applying learning algorithms on a given data so as determine what class an unlabelled data having same pattern belongs to. However, algorithms do not learn properly when a massive difference in size between data classes exist. This classification problem exists in many real world application domains and has been a popular...
As a graph-based clustering approach, dominant sets clustering determines the number of clusters automatically and possesses some other nice properties. By applying histogram equalization transformation to the similarity matrix before clustering, we are able to accomplish the dominant sets clustering process without any user-specified parameters. However, this transformation usually leads to over-segmented...
Kernel functions based machine learning algorithms have been extensively studied over the past decades with successful applications in a variety of real-world tasks. In this paper, we formulate a kernel level composition method to embed multiple local classifiers (kernels) into one kernel function, so as to obtain a more flexible data-dependent kernel. Since such composite kernels are composed by...
Spectral clustering has shown a superior performance in analyzing the cluster structure. However, the exponentially computational complexity limits its application in analyzing large-scale data. To tackle this problem, many low-rank matrix approximating algorithms are proposed, of which the Nyström method is an approach with proved lower approximate errors. The algorithms commonly combine two powerful...
In order to improve the identification accuracy of the online classifier in binary classification problems, we here propose complex-valued online machine learning algorithms. The Perceptron is commonly used in simple form of online machine learning. First, we extended it to the complex domain. The real-valued inputs are projected to points on the first quarter of unit circle on the complex plane....
The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. Most of them, however, are greedy algorithms that have the drawback of obtaining only local optimums. Besides, conventional split criteria they used, e.g. Shannon entropy,...
Abnormal joint moments during gait are validated predictors of knee pain in osteoarthritis. Calculation of moments necessitates measurement of forces and moment arms about joints during walking. Dynamically changing moment arms can be calculated from motion trackers either optically or with wireless inertia sensing units, but the measurement of forces is more problematic. Either the patient has to...
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