9531801
Improving the Performance of Gene Mention Recognition System using Reformed Lexicon-based Support Vector Machine
In this paper, we propose a gene mention recognition system for biomedical literature using Support Vector Machine based on a reformed lexicon. Then we present an ensemble of rule-based post-processing modules, a integrity check module, a boundary check module, an abbreviation resolution module and a name pruning module, to improve the performance further. The newly developed lexicon is composed of uni-indicating and co-indicating words inside gene mention phrases. With the carefully designed lexicon, the characters of gene mentions can be extracted to support the recognition. Based on this lexicon and post-processing modules, our system can recognize gene mentions in biomedical literature with fairly high accuracy, which can achieve the precision of 85.07%, recall of 83.68% and balanced Fβ=1 score of 84.37.
36831968
Evaluating and Comparing Biomedical Term Identification Systems
In this paper, we propose a term identification system using conditional random fields (CRFs) on two biomedical datasets. Through employing several sets of experiments, we make a comprehensive investigation for different types of features. The final experimental results reflect that with carefully designed features i.e., adding not only the individual and dynamic features but also the combinational features, our system can identify biomedical terms with fairly high accuracy on both datasets, compared with other top systems already published in the literature.
9822006
Named Entity Recognition in Biomedical Literature Using Two-Layer Support Vector Machines
A beam receiver secured to an upper portion of a blade of an earth working equipment can rotate in a horizontal plane and move in a vertical direction and, accordingly, can catch a laser beam from a beam projector regardless of a direction of the equipment. When the beam has been deviated out of the beam receiver, the direction of the deviation is detected so that the beam can be received by the beam receiver again. The height of the beam receiver relative to the blade can be adjusted and the level of the blade can thereby be controlled very accurately.
35820562
Named Entity Recognition in Biomedical Literature: A Comparison of Support Vector Machines and Conditional Random Fields
In this paper, we propose two named entity recognition systems for biomedical literature, System1 using support vector machines and System2 using conditional random fields. Through employing several sets of experiments, we make a comprehensive comparison between these two systems. The final results reflect that System2 can achieve higher accuracy than System1, because System2 can catch more essential properties by handling the richer set of features, i.e., adding not only the individual and dynamic features as System1 does but also the combinational features, which can improve the performance further. Furthermore, with carefully designed features, System2 can recognize named entities in biomedical literature with fairly high accuracy, which can achieve the precision of 89.43%, recall of 83.32% and balanced F β= 1 score of 86.28%.
13345001
Context-Sensitive Kernel Functions: A Distance Function Viewpoint
This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted) distance functions. It is shown how a weighted string distance can be decomposed and subsequently used in different kernel functions and how these kernel functions correspond to real kernels between the continuous, real representations of the symbolic, context-based representations of the vectors.
1280778
Splice Site Prediction using Support Vector Machines with Context-Sensitive Kernel Functions
This paper focuses on the use of support vector machines on a typical context-dependent classification task, splice site prediction. For this type of problems, it has been shown that a context-based approach should be preferred over a transfor- mation approach because the former approach can easily incorporate statistical mea- sures or directly plug sensitivity information into distance functions. In this paper, we designed three types of context-sensitive kernel functions: polynomial-based, radial basis function-based and negative distance-based kernels. From the experimental re- sults it becomes clear that the radial basis function-based kernel with information gain weighting gets the best accuracies and can always outperform their simple non-sensitive counterparts both in accuracy and in model complexity. And with well designed fea- tures and carefully chosen context sizes, our system can predict splice sites with fairly high accuracy, which can achieve the FP95% rate, 3.94 for donor sites and 5.98 for acceptor sites, an approximate state of the art performance for the moment.
5701196
An Architecture for Supporting Network Fault Recovery Management
Highly available and resilient networks play a decisive role in today's networked world. As network faults are inevitable and networks are becoming constantly intricate, finding effective fault recovery solutions in a timely manner is becoming a challenging task for administrators. Therefore, an automated mechanism to support fault resolution is essential towards efficient fault handling process. In this paper we propose an architecture to support automated fault recovery in terms of traffic engineering, recovery knowledge discovery and automated recovery planning. We base our discussion on an application scenario for recovery from border router failure to maintain optimized configuration of outbound inter-domain traffic.
4963124
Blind equalization Using ν- support vector regressor for constant modulus signals
The support vector machine has been recently developed for blind equalization of constant modulus signals. In this paper we propose to use a v-support vector regressor (nu-SVR) for blindly equalizing multipath channels because of the high generalization ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind nu-SVR equalizer. The performance of the proposed algorithm is analyzed by means of computer simulations.
1975396
An Improved Greedy Bayesian Network Learning Algorithm on Limited Data
Although encouraging results have been reported, existing Bayesian network (BN) learning algorithms have some troubles on limited data. A statistical or information theoretical measure or a score function may be unreliable on limited datasets, which affects learning accuracy. To alleviate the above problem, we propose a novel BN learning algorithm MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm applies Max Relevance and Min Redundancy feature selection technique and proposes Local Bayesian Increment (LBI) function according to the Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Experimental results show that MRMRG algorithm has much better accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.
15250571
Frequent Itemset Mining Based on Heuristic Two Level Counting
Recently, many enchanced Apriori algorithms have been proposed to efficiently generate all frequent itemsets from datasets in data mining field. Although efficient techniques were presented, those algorithms are either time-consuming or memory-consuming. To address the issue further, a new algorithm, which introduced a novel support counting method, heuristic two level counting, is proposed. HTLC method adopts an improved itemset generating technology in the generation process of low level itemsets, which promotes the production of low level frequent itemsets or candidate itemsets. It also applies a heuristic traversal technology which speeds up one pass over datasets and support counting technology which largely reduces the number of passes over datasets to the generation of high level frequent itemsets. Finally, the experimental results show that it outperforms existing Apriori-like algorithms in mostly datasets
16553815
A Robust Algorithm for Video Based Human Motion Tracking
In this paper, we present a robust algorithm to capture rapid human motion with self-occlusion. Instead of predicting the position of each human feature, the interest-region of full body is estimated. Then candidate features are extracted through the overall search in the interest-region. To establish the correspondence between candidate features and actual features, an adaptive Bayes classifier is constructed based on the time-varied models of feature attributions. At last, a hierarchical human feature model is adopted to verify and accomplish the feature correspondence. To improve the efficiency, we propose a multiresolution search strategy: the initial candidate feature set is estimated at the low resolution image and successively refined at higher resolution levels. The experiment demonstrates the effectiveness of our algorithm.