A Training Sample Sequence Planning Method for Pattern Recognition Problems

In solving pattern recognition problems, many classification methods, such as the nearest-neighbor (NN) rule, need to determine prototypes from a training set. To improve the performance of these classifiers in finding an efficient set of prototypes, this paper introduces a training sample sequence planning method. In particular, by estimating the relative nearness of the training samples to the decision boundary, the approach proposed here incrementally increases the number of prototypes until the desired classification accuracy has been reached. This approach has been tested with a NN classification method and a neural network training approach. Studies based on both artificial and real data demonstrate that higher classification accuracy can be achieved with fewer prototypes

A False Acceptance Error Controlling Method for Hyperspherical Classifiers

Controlling false acceptance errors is of critical importance in many pattern recognition applications, including signature and speaker verification problems. Toward this goal, this paper presents two post-processing methods to improve the performance of hyperspherical classifiers in rejecting patterns from unknown classes. The first method uses a self-organizational approach to design minimum radius hyperspheres, reducing the redundancy of the class region defined by the hyperspherical classifiers. The second method removes additional redundant class regions from the hyperspheres by using a clustering technique to generate a number of smaller hyperspheres. Simulation and experimental results demonstrate that by removing redundant regions these two post-processing methods can reduce the false acceptance error without significantly increasing the false rejection error

One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems

Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods

A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm

THE CORRELATION OF GOLF PUTTING CLUB HEAD VELOCITY AND GRIP FORCE FOR EACH PHASE

We investigate the correlation of golf putting club head velocity and grip force in different phases during the putting stroke. Five elite college players (handicap: 2~8) executed a putt as accurately as possible to reach a target distance of 12ft. The Novel System and were used to measure the grip force and club head velocity. The lowest club head velocity and grip force both occurred at address up to the top of backswing (phase I). The club head velocity and grip force started increasing during the downswing and reached its peak before impact (phase II), and decreased after impact to finish (phase III). The mean club head velocity and grip force for Phase I, II, III in order are 0.33m/s, 0.92m/s, 0.87m/s; 28.09N, 54.77N, 50.76N. Club head velocity was significantly correlated to grip force in phase II and III (r=0.937; r=0.866). The similar variation pattern of club head speed and grip force may give better control to the putter during the impact and produce more consistent putting stroke

The significance of seizures and other predictive factors during the acute illness for the long-term outcome after bacterial meningitis

SummaryBackgroundSeizures are important neurological complications of bacterial meningitis, but no information about its epidemiology and the outcomes of seizures after community-acquired bacterial meningitis (CABM) in an adult population have been reported.AimsTo determine the frequency, clinical relevance, subtypes of seizures during the acute phase of bacterial meningitis, and the long-term outcomes of seizure complicating adult CABM.MethodsIn this 12-year retrospective study, 117 adult patients were identified with culture-proven CABM. A comparison was made between the clinical data of the patients with and without seizures during hospitalization.ResultsThirty-one patients had seizures during CABM, accounting for 27% (31/117) of the episodes. The time interval between the onset of bacterial meningitis and the seizures was 1–21 days (mean, 4 days). Furthermore, 80% (25/31) of the episodes occurred within 24h of presentation. Ten patients who had seizures progressed to status epilepticus. At follow-up after completing treatment, 10 patients completely recovered and were seizure-free, 19 died of meningitis during the acute stage and the other two progressed to chronic epilepsy.ConclusionA log-rank test demonstrated that the long-term outcome of adult CABM with acute seizures produced worse outcomes than for those who had no seizures, though no difference was noted between focal and generalized seizures. None of our patients without seizures in the acute phase of bacterial meningitis developed late seizures during the follow-up periods. Poor outcome in this study may attribute to neurological complications such as seizure, hydrocephalus, infection itself, or a combination of complications

Impact of body-mass factors on setup displacement in patients with head and neck cancer treated with radiotherapy using daily on-line image guidance

BACKGROUND: To determine the impact of body-mass factors (BMF) before radiotherapy and changes during radiotherapy on the magnitude of setup displacement in patients with head and neck cancer (HNC). METHODS: The clinical data of 30 patients with HNC was analyzed using the alignment data from daily on-line on-board imaging from image-guided radiotherapy. BMFs included body weight, body height, and the circumference and bilateral thickness of the neck. Changes in the BMFs during treatment were retrieved from cone beam computed tomography at the 10th and 20th fractions. Setup errors for each patient were assessed by systematic error (SE) and random error (RE) through the superior-inferior (SI), anterior-posterior (AP), and medial-lateral (ML) directions, and couch rotation (CR). Using the median values of the BMFs as a cutoff, the impact of the factors on the magnitude of displacement was assessed by the Mann–Whitney U test. RESULTS: A higher body weight before radiotherapy correlated with a greater AP-SE (p = 0.045), SI-RE (p = 0.023), and CR-SE (p = 0.033). A longer body height was associated with a greater SI-RE (p = 0.002). A performance status score of 1 or 2 was related to a greater AP-SE (p = 0.043), AP-RE (p = 0.015), and SI-RE (p = 0.043). Among the ratios of the BMFs during radiotherapy, the values at the level of mastoid tip at the 20(th) fraction were associated with greater setup errors. CONCLUSIONS: To reduce setup errors in patients with HNC receiving RT, the use of on-line image-guided radiotherapy is recommended for patients with a large body weight or height, and a performance status score of 1–2. In addition, adaptive planning should be considered for those who have a large reduction ratio in the circumference (<1) and thickness (<0.94) over the level of the mastoid tip during the 20(th) fraction of treatment