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Tutorial on Support Vector Machine
Issue:
Volume 6, Issue 4-1, July 2017
Pages:
1-15
Received:
7 September 2015
Accepted:
8 September 2015
Published:
17 June 2016
DOI:
10.11648/j.acm.s.2017060401.11
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Abstract: Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on theory of optimization which is the base of support vector machine.
Abstract: Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest ...
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Tutorial on Hidden Markov Model
Issue:
Volume 6, Issue 4-1, July 2017
Pages:
16-38
Received:
11 September 2015
Accepted:
13 September 2015
Published:
17 June 2016
DOI:
10.11648/j.acm.s.2017060401.12
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Abstract: Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on HMM with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on three common problems of HMM such as evaluation problem, uncovering problem, and learning problem, in which learning problem with support of optimization theory is the main subject.
Abstract: Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on...
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Longest-path Algorithm to Solve Uncovering Problem of Hidden Markov Model
Issue:
Volume 6, Issue 4-1, July 2017
Pages:
39-47
Received:
12 March 2016
Accepted:
14 March 2016
Published:
17 June 2016
DOI:
10.11648/j.acm.s.2017060401.13
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Abstract: Uncovering problem is one of three main problems of hidden Markov model (HMM), which aims to find out optimal state sequence that is most likely to produce a given observation sequence. Although Viterbi is the best algorithm to solve uncovering problem, I introduce a new viewpoint of how to solve HMM uncovering problem. The proposed algorithm is called longest-path algorithm in which the uncovering problem is modeled as a graph. So the essence of longest-path algorithm is to find out the longest path inside the graph. The optimal state sequence which is solution of uncovering problem is constructed from such path.
Abstract: Uncovering problem is one of three main problems of hidden Markov model (HMM), which aims to find out optimal state sequence that is most likely to produce a given observation sequence. Although Viterbi is the best algorithm to solve uncovering problem, I introduce a new viewpoint of how to solve HMM uncovering problem. The proposed algorithm is ca...
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Comparison of Singular Perturbations Approximation Method and Meta-Heuristic-Based Techniques for Order Reduction of Linear Discrete Systems
Issue:
Volume 6, Issue 4-1, July 2017
Pages:
48-54
Received:
16 August 2016
Accepted:
12 September 2016
Published:
8 December 2016
DOI:
10.11648/j.acm.s.2017060401.14
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Abstract: This paper presents a survey of Singular Perturbations Approximation (SPA) method and meta-heuristic techniques for order reduction of linear systems in discrete case. A comparison of intelligent techniques to determine the reduced order model of higher order linear systems is presented. Two approaches are considered: Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). These methods are employed to reduce the higher order model and based on the minimization of the Mean Square Error (MSE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Each method is illustrated through numerical examples.
Abstract: This paper presents a survey of Singular Perturbations Approximation (SPA) method and meta-heuristic techniques for order reduction of linear systems in discrete case. A comparison of intelligent techniques to determine the reduced order model of higher order linear systems is presented. Two approaches are considered: Particle Swarm Optimization (P...
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Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis
Tsair-Wei Chien,
Shih-Bin Su
Issue:
Volume 6, Issue 4-1, July 2017
Pages:
55-63
Received:
20 December 2016
Accepted:
9 January 2017
Published:
24 January 2017
DOI:
10.11648/j.acm.s.2017060401.15
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Abstract: Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through SNA is the disease transmission path of Middle East respiratory syndrome (MERS). Purpose: To determine key roles using structure holes of SNA for further improvement, and to show the SNA advantage over traditional classic test theory. Methods: Data were records regarding 443 adult mentally retarded residents who were infected with amoebiasis and distributed in 10 houses in past 10 years. A series of intensive mass screenings and treatment interventions were conducted. Structure holes were applied to verify the efficacy of determining key roles and strong associations for the domains of interest in a network and compared with the result obtained from the traditional Chi-square statistics. Results: The classification of key roles in a network (e.g., with which year the residency room with amoebiasis cases has strongly association) can be effectively discriminated through the structure holes of SNA. Though the result is similar to the traditional Chi-square statistics, the structure holes can release much more useful and valuable information for further investigation. Conclusions: Because of advances in computer technology, the number of healthcare studies for the group classification and association assertion continues to increase and benefit comparisons of data if structure holes of SNA are applied.
Abstract: Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through S...
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Mobile Online Computer-Adaptive Tests (CAT) for Gathering Patient Feedback in Pediatric Consultations
Tsair-Wei Chien,
Wen-Pin Lai,
Ju-Hao Hsieh
Issue:
Volume 6, Issue 4-1, July 2017
Pages:
64-71
Received:
19 December 2016
Accepted:
9 January 2017
Published:
6 February 2017
DOI:
10.11648/j.acm.s.2017060401.16
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Abstract: Background: Few studies have used online patient feedback from smartphones for computer adaptive testing (CAT). Objective: We developed a mobile online CAT survey procedure and evaluated whether it was more precise and efficient than traditional non-adaptive testing (NAT) when gathering patient feedback about their perceptions of interaction with a physician after a consultation. Method: Two hundred proxy participants (parents or guardians) were recruited to respond to twenty 5-point questions (the P4C_20 scale) about perceptions of doctor-patient and doctor-family interaction in clinical pediatric consultations. Through the parameters calibrated using a Rasch partial credit model (PCM) and a Rasch rating scale model (RSM), two paired comparisons of empirical and simulation data were administered to calculate and compare the efficiency and precision of CAT and NAT in terms of shorter item length and fewer counts of difference number ratio (< 5%) using independent t tests. An online CAT was designed using two modes of PCM and RSM for use in clinical settings. Results: The graphical online CAT for smartphones used by the parents or guardians of pediatric hospital patients was more efficient and no less precise than NAT. Conclusions: CAT-based administration of the P4C_20 substantially reduced respondent burden without compromising measurement precision.
Abstract: Background: Few studies have used online patient feedback from smartphones for computer adaptive testing (CAT). Objective: We developed a mobile online CAT survey procedure and evaluated whether it was more precise and efficient than traditional non-adaptive testing (NAT) when gathering patient feedback about their perceptions of interaction with a...
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Global Optimization with Descending Region Algorithm
Issue:
Volume 6, Issue 4-1, July 2017
Pages:
72-82
Received:
8 April 2017
Accepted:
10 April 2017
Published:
9 June 2017
DOI:
10.11648/j.acm.s.2017060401.17
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Abstract: Global optimization is necessary in some cases when we want to achieve the best solution or we require a new solution which is better the old one. However global optimization is a hazard problem. Gradient descent method is a well-known technique to find out local optimizer whereas approximation solution approach aims to simplify how to solve the global optimization problem. In order to find out the global optimizer in the most practical way, I propose a so-called descending region (DR) algorithm which is combination of gradient descent method and approximation solution approach. The ideology of DR algorithm is that given a known local minimizer, the better minimizer is searched only in a so-called descending region under such local minimizer. Descending region is begun by a so-called descending point which is the main subject of DR algorithm. Descending point, in turn, is solution of intersection equation (A). Finally, I prove and provide a simpler linear equation system (B) which is derived from (A). So (B) is the most important result of this research because (A) is solved by solving (B) many enough times. In other words, DR algorithm is refined many times so as to produce such (B) for searching for the global optimizer. I propose a so-called simulated Newton – Raphson (SNR) algorithm which is a simulation of Newton – Raphson method to solve (B). The starting point is very important for SNR algorithm to converge. Therefore, I also propose a so-called RTP algorithm, which is refined and probabilistic process, in order to partition solution space and generate random testing points, which aims to estimate the starting point of SNR algorithm. In general, I combine three algorithms such as DR, SNR, and RTP to solve the hazard problem of global optimization. Although the approach is division and conquest methodology in which global optimization is split into local optimization, solving equation, and partitioning, the solution is synthesis in which DR is backbone to connect itself with SNR and RTP.
Abstract: Global optimization is necessary in some cases when we want to achieve the best solution or we require a new solution which is better the old one. However global optimization is a hazard problem. Gradient descent method is a well-known technique to find out local optimizer whereas approximation solution approach aims to simplify how to solve the gl...
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