Search

In this paper, a new auto-scoring algorithm that automatically evaluates the Berg balance scale (BBS) tasks is proposed. The BBS can be used as an indicator for patients to analyze their rehabilitation status by themselves. In the proposed method, the patient must use a wearable inertial measurement unit (IMU) sensor, and the result of the patient's BBS task execution would be scored automatically by the evaluation algorithm. The proposed evaluation algorithm involves only few computations and has high scoring accuracy. Nonlinear kernel principal component analysis and a small number of linear features were combined to reduce the features from each sensor, and the algorithm model was implemented using a support vector machine (SVM), a machine learning technique with low computational complexity. The effectiveness of the algorithm was evaluated through clinical evaluation of 53 subjects with up to eight IMU sensors. The average accuracy of the proposed algorithm using eight sensors was 93.2%, and that using five sensors was 91.5%. There was a 12.6% and 10.9% increase in accuracy, respectively, compared to a previous study. The training and testing times of the proposed SVM model were over 38 times faster than the multi-layer perceptron model used in a previous study. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of automata algorithms to machine learning. It introduces the first optimization solution for support vector machines used with sequence kernels that is purely based on weighted automata and transducer algorithms, without requiring any specific solver. The algorithms presented apply to a family of kernels covering all those commonly used in text and speech processing or computational biology. We show that these algorithms have significantly better computational complexity than previous ones and report the results of large-scale experiments demonstrating a dramatic reduction of the training time, typically by several orders of magnitude. [ABSTRACT FROM AUTHOR]

Many problems in pattern classification and feature extraction involve dimensionality reduction as a necessary processing. Traditional manifold learning algorithms, such as ISOMAP, LLE, and Laplacian Eigenmap, seek the low-dimensional manifold in an unsupervised way, while the local discriminant analysis methods identify the underlying supervised submanifold structures. In addition, it has been well-known that the intraclass null subspace contains the most discriminative information if the original data exist in a high-dimensional space. In this paper, we seek for the local null space in accordance with the null space LDA (NLDA) approach and reveal that its computational expense mainly depends on the quantity of connected edges in graphs, which may be still unacceptable if a great deal of samples are involved. To address this limitation, an improved local null space algorithm is proposed to employ the penalty subspace to approximate the local discriminant subspace. Compared with the traditional approach, the proposed method can achieve more efficiency so that the overload problem is avoided, while slight discriminant power is lost theoretically. A comparative study on classification shows that the performance of the approximative algorithm is quite close to the genuine one. [ABSTRACT FROM AUTHOR]

The work presented in this paper is motivated by the practical need for content extraction, and the available data source and evaluation benchmark from the ACE program. The Chinese entity detection and tracking task is of particular interest to us. A novel solution is proposed to alleviate the language-independent and language-dependent problems special in this task. Mention detection takes advantages of machine learning approaches and character-based models. It manipulates different types of entities being mentioned and different constitution units (i.e., extents and heads) separately. Mentions referring to the same entity are linked together by integrating most-specific-first and closest-first rule based pairwise clustering algorithms. Types of mentions and entities are determined by head-driven classification approaches. The implemented system achieves 66.1 of ACE value, which has been one of the top-tier results. [ABSTRACT FROM AUTHOR]

Recently kernel methods in support vector machines have widely been used in machine learning algorithms to obtain nonlinear models. Clustering is an unsupervised learning method which divides whole data set into subgroups, and popular clustering algorithms such as c-means are employing kernel methods. Other kernel-based clustering algorithms have been inspired from kernel c-means. However, the formulation of kernel c-means has a high computational complexity. This paper gives an alternative formulation of kernel-based clustering algorithms derived from competitive learning clustering. This new formulation obviously uses sequential updating or on-line learning to avoid high computational complexity. We apply kernel methods to related algorithms: learning vector quantization and self-organizing map. We moreover consider kernel methods for sequential c-means and its fuzzy version by the proposed formulation. [ABSTRACT FROM AUTHOR]