Relevant bibliographies by topics / Decision algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Decision algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Decision algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Decision algorithm"

1

Parlindungan and HariSupriadi. "Implementation Decision Tree Algorithm for Ecommerce Website." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 13, 2020): 3611–14. http://dx.doi.org/10.37200/ijpr/v24i2/pr200682.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Antyufeev, S. V. "Fuzzy decision algorithm." Programming and Computer Software 32, no. 6 (December 2006): 317–23. http://dx.doi.org/10.1134/s0361768806060041.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ma, Xiuqin, Yanan Wang, Hongwu Qin, and Jin Wang. "A Decision-Making Algorithm Based on the Average Table and Antitheses Table for Interval-Valued Fuzzy Soft Set." Symmetry 12, no. 7 (July 7, 2020): 1131. http://dx.doi.org/10.3390/sym12071131.

Full text
Abstract:
Interval-valued fuzzy soft set is one efficient mathematical model employed to handle the uncertainty of data. At present, there exist two interval-valued fuzzy soft set-based decision-making algorithms. However, the two existing algorithms are not applicable in some cases. Therefore, for the purpose of working out this problem, we propose a new decision-making algorithm, based on the average table and the antitheses table, for this mathematical model. Here, the antitheses table has symmetry between the objects. At the same time, an example is designed to prove the availability of our algorithm. Later, we compare our proposed algorithm with the two existing decision-making algorithms in several cases. The comparison result shows that only our proposed algorithm can make an effective decision in exceptional cases, and the other two methods cannot make decisions. It is therefore obvious that our algorithm has a stronger decision-making ability, thus further demonstrating the feasibility of our algorithm. In addition, a real data set of the homestays in Siming District, Xiamen is provided to further corroborate the practicability of our algorithm in a realistic situation.
APA, Harvard, Vancouver, ISO, and other styles
4

Chen, Yi Rui, and Yi Zhuang. "An Adaptive Decision Concurrency Control Algorithm." Advanced Materials Research 1046 (October 2014): 512–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.512.

Full text
Abstract:
For the lack of adaptability about the existing concurrency control algorithms, adaptive decision concurrency control algorithm is proposed. ADCC algorithm divides concurrency control process into two phases in: execution authorizing phase and strategy selecting phase. In execution authorizing phase, algorithm compares statistics and effectiveness of transactions to determine the execution order of conflict transactions. In strategy selecting phase, according to transactions’ read/write status and current conflict rate, algorithm selects optimistic/pessimistic conflict resolution strategy adaptively. Such selection mechanism makes ADCC algorithm have high efficiency no matter database system is busy or idle. Simulation experiment proves that ADCC algorithm this paper proposed is superior to classical strict two phases locking algorithm and hybrid concurrency control. So ADCC algorithm performs well in the period of concurrency control.
APA, Harvard, Vancouver, ISO, and other styles
5

Green, Ben, and Yiling Chen. "Algorithm-in-the-Loop Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13663–64. http://dx.doi.org/10.1609/aaai.v34i09.7115.

Full text
Abstract:
We introduce a new framework for conceiving of and studying algorithms that are deployed to aid human decision making: “algorithm-in-the-loop” systems. The algorithm-in-the-loop framework centers human decision making, providing a more precise lens for studying the social impacts of algorithmic decision making aids. We report on two experiments that evaluate algorithm-in-the-loop decision making and find significant limits to these systems.
APA, Harvard, Vancouver, ISO, and other styles
6

Chen, Li Fang, and Ying Ma. "Improved Algorithm for Discretization of Decision Table." Advanced Materials Research 532-533 (June 2012): 1649–53. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1649.

Full text
Abstract:
Discretization of decision table is the important step for pretreatment of data mining and machine learning, which related to the effect of learning. It has great contribution to speeding up the followed learning algorithms, cutting down the real demand of algorithms on running space and time. In this paper, the basic characteristics and framework of discretization approaches about greedy and improved algorithm are analyzed at first, then a new algorithm is put forward to select the useful cuts. The example is given to show that the useful cuts is consistent with the result of technicist. The algorithm offered the important theoretics basis for followed attribute reduction.
APA, Harvard, Vancouver, ISO, and other styles
7

Jin, Xiaomin, Zhongmin Wang, and Wenqiang Hua. "Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing." Mobile Information Systems 2019 (September 17, 2019): 1–17. http://dx.doi.org/10.1155/2019/8049804.

Full text
Abstract:
Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.
APA, Harvard, Vancouver, ISO, and other styles
8

PURDILA, V., and S. G. PENTIUC. "Fast Decision Tree Algorithm." Advances in Electrical and Computer Engineering 14, no. 1 (2014): 65–68. http://dx.doi.org/10.4316/aece.2014.01010.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Khuri, Sami, and Aida Batarekh. "A binary decision algorithm." Information Sciences 53, no. 3 (February 1991): 251–70. http://dx.doi.org/10.1016/0020-0255(91)90039-w.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kirandeep, Kirandeep, and Prof Neena Madan. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 740–44. http://dx.doi.org/10.31142/ijtsrd11073.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Decision algorithm"

1

Gato, Gonçalo. "Algorithm and decision in musical composition." Thesis, Guildhall School of Music and Drama, 2016. http://openaccess.city.ac.uk/17292/.

Full text
Abstract:
Through a series of creative projects this doctorate set out to research how computer-assisted composition (CAC) of music affects decision-making in my compositional practice. By reporting on the creative research journey, this doctorate is a contribution towards a better understanding of the implications of CAC by offering new insights into the composing process. It is also a contribution to the composition discipline as new techniques were devised, together with new applications of existing techniques. Using OpenMusic as the sole programming environment, the manual/machine interface was explored through different balances between manual and algorithmic composition and through aesthetic reflection guiding the composing process. This helped clarify the purpose, adequacy and nature of each method as decisions were constantly being taken towards completing the artistic projects. The most suitable use of algorithms was as an environment for developing, testing, refining and assessing compositional techniques and the music materials they generate: a kind of musical laboratory. As far as a technique can be described by a set of rules, algorithms can help formulate and refine it. Also capable of incorporating indeterminism, they can act as powerful devices in discovering unforeseen musical implications and results. Algorithms alone were found to be insufficient to simulate human creative thought because aspects such as (but not limited to) imagination, judgement and personal bias could only, and hypothetically, be properly simulated by the most sophisticated forms of artificial intelligence. Furthermore, important aspects of composition such as instrumentation, articulation and orchestration were not subjected to algorithmic treatment because, not being sufficiently integrated in OpenMusic currently, they would involve a great deal of knowledge to be specified and adapted to computer language. These shortcomings of algorithms, therefore, implied varying degrees of manual interventions to be carried out on raw materials coming out of their evaluations. A combination of manual and algorithmic composition was frequently employed so as to properly handle musical aspects such as cadence, discourse, monotony, mechanicalness, surprise, and layering, among others. The following commentary illustrates this varying dialogue between automation and intervention, placing it in the wider context of other explorations at automating aspects of musical composition.
APA, Harvard, Vancouver, ISO, and other styles
2

Kassim, M. E. "Elliptical cost-sensitive decision tree algorithm (ECSDT)." Thesis, University of Salford, 2018. http://usir.salford.ac.uk/47191/.

Full text
Abstract:
Cost-sensitive multiclass classification problems, in which the task of assessing the impact of the costs associated with different misclassification errors, continues to be one of the major challenging areas for data mining and machine learning. The literature reviews in this area show that most of the cost-sensitive algorithms that have been developed during the last decade were developed to solve binary classification problems where an example from the dataset will be classified into only one of two available classes. Much of the research on cost-sensitive learning has focused on inducing decision trees, which are one of the most common and widely used classification methods, due to the simplicity of constructing them, their transparency and comprehensibility. A review of the literature shows that inducing nonlinear multiclass cost-sensitive decision trees is still in its early stages and further research could result in improvements over the current state of the art. Hence, this research aims to address the following question: 'How can non-linear regions be identified for multiclass problems and utilized to construct decision trees so as to maximize the accuracy of classification, and minimize misclassification costs?' This research addresses this problem by developing a new algorithm called the Elliptical Cost-Sensitive Decision Tree algorithm (ECSDT) that induces cost-sensitive non-linear (elliptical) decision trees for multiclass classification problems using evolutionary optimization methods such as particle swarm optimization (PSO) and Genetic Algorithms (GAs). In this research, ellipses are used as non-linear separators, because of their simplicity and flexibility in drawing non-linear boundaries by modifying and adjusting their size, location and rotation towards achieving optimal results. The new algorithm was developed, tested, and evaluated in three different settings, each with a different objective function. The first considered maximizing the accuracy of classification only; the second focused on minimizing misclassification costs only, while the third considered both accuracy and misclassification cost together. ECSDT was applied to fourteen different binary-class and multiclass data sets and the results have been compared with those obtained by applying some common algorithms from Weka to the same datasets such as J48, NBTree, MetaCost, and the CostSensitiveClassifier. The primary contribution of this research is the development of a new algorithm that shows the benefits of utilizing elliptical boundaries for cost-sensitive decision tree learning. The new algorithm is capable of handling multiclass problems and an empirical evaluation shows good results. More specifically, when considering accuracy only, ECSDT performs better in terms of maximizing accuracy on 10 out of the 14 datasets, and when considering minimizing misclassification costs only, ECSDT performs better on 10 out of the 14 datasets, while when considering both accuracy and misclassification costs, ECSDT was able to obtain higher accuracy on 10 out of the 14 datasets and minimize misclassification costs on 5 out of the 14 datasets. The ECSDT also was able to produce smaller trees when compared with J48, LADTree and ADTree.
APA, Harvard, Vancouver, ISO, and other styles
3

Ogunsanya, Oluwole Victor. "Decision support using Bayesian networks for clinical decision making." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688.

Full text
Abstract:
This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly, it demonstrates the flexibility of the Dynamic Discretization Algorithm in modeling existing medical knowledge using appropriate statistical distributions. Many practical applications of BNs use the relative frequency approach while translating existing medical knowledge to a prior distribution in a BN model. This approach does not capture the full uncertainty surrounding the prior knowledge. Secondly, it demonstrates a novel use of the multinomial BN formulation in learning parameters of categorical variables. The traditional approach requires fixed number of parameters during the learning process but this framework allows an analyst to generate a multinomial BN model based on the number of parameters required. Thirdly, it presents a novel application of the multinomial BN formulation and dynamic discretization to learning causal relations between variables. The idea is to consider competing causal relations between variables as hypotheses and use data to identify the best hypothesis. The result shows that BN models can provide an alternative to the conventional causal learning techniques. The fourth novel application is the use of Hierarchical Bayesian Network (HBN) models, augmented by dynamic discretization technique, to meta-analysis of clinical data. The result shows that BN models can provide an alternative to classical meta analysis techniques. The thesis presents two clinical case studies to demonstrate these novel applications of BN models. The first case study uses data from a multi-disciplinary team at the Royal London hospital to demonstrate the flexibility of the multinomial BN framework in learning parameters of a clinical model. The second case study demonstrates the use of BN and dynamic discretization to solving decision problem. In summary, the combination of the Junction Tree Algorithm and Dynamic Discretization Algorithm provide a unified modeling framework for solving interesting clinical problems.
APA, Harvard, Vancouver, ISO, and other styles
4

Bacak, Hikmet Ozge. "Decision Making System Algorithm On Menopause Data Set." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12612471/index.pdf.

Full text
Abstract:
Multiple-centered clustering method and decision making system algorithm on menopause data set depending on multiple-centered clustering are described in this study. This method consists of two stages. At the first stage, fuzzy C-means (FCM) clustering algorithm is applied on the data set under consideration with a high number of cluster centers. As the output of FCM, cluster centers and membership function values for each data member is calculated. At the second stage, original cluster centers obtained in the first stage are merged till the new numbers of clusters are reached. Merging process relies upon a &ldquo
similarity measure&rdquo
between clusters defined in the thesis. During the merging process, the cluster center coordinates do not change but the data members in these clusters are merged in a new cluster. As the output of this method, therefore, one obtains clusters which include many cluster centers. In the final part of this study, an application of the clustering algorithms &ndash
including the multiple centered clustering method &ndash
a decision making system is constructed using a special data on menopause treatment. The decisions are based on the clusterings created by the algorithms already discussed in the previous chapters of the thesis. A verification of the decision making system / v decision aid system is done by a team of experts from the Department of Department of Obstetrics and Gynecology of Hacettepe University under the guidance of Prof. Sinan Beksaç
.
APA, Harvard, Vancouver, ISO, and other styles
5

Shi, Haijian. "Best-first Decision Tree Learning." The University of Waikato, 2007. http://hdl.handle.net/10289/2317.

Full text
Abstract:
In best-first top-down induction of decision trees, the best split is added in each step (e.g. the split that maximally reduces the Gini index). This is in contrast to the standard depth-first traversal of a tree. The resulting tree will be the same, just how it is built is different. The objective of this project is to investigate whether it is possible to determine an appropriate tree size on practical datasets by combining best-first decision tree growth with cross-validation-based selection of the number of expansions that are performed. Pre-pruning, post-pruning, CART-pruning can be performed this way to compare.
APA, Harvard, Vancouver, ISO, and other styles
6

Bai, Ming. "Optimization decision maker algorithm for infrastructure interdependencies with I2Sim applications." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42809.

Full text
Abstract:
The study of complex interdependent systems is an important research area. In recent years, it has been applied to disaster response management and building energy systems. I2Sim (Infrastructures Interdependencies Simulator) is a software simulation toolbox developed by the Power Lab at the University of BC. It has a wide range of capabilities including simulation of disasters scenarios and energy system optimization. The user needs to provide Human Readable Tables (HRTs) as inputs for the program. The basic ontology of the I2Sim Resource Layer includes cells, channels and tokens, which are abstractions from real life objects. Initially, the intent of this thesis was to examine the energy usage pattern of the Kaiser Building, perform energy optimization modeling and examine how it relates to energy policies. After some initial research, it was not possible to proceed further due to a lack of metered data. The research focus was changed to disaster scenario simulation. This thesis proposes a new optimization algorithm named Lagrange Based Optimization (LBO). The main objective is to maximize the number of discharged patients from the hospitals simulated in this study. The first scenario modeled is a three-hospital scenario with no transportation to illustrate the principles of the algorithm. Then a three-venue three-hospital scenario with transportation was modeled to maximize both the number of patients transported to the hospitals and the number of patients discharged from the hospitals. After that, the first scenario is compared against the performance of a Reinforcement Learning (RL) agent method concurrently developed in the same research group. Overall, the LBO algorithm demonstrates optimal results in the various I2Sim modeling scenarios.
APA, Harvard, Vancouver, ISO, and other styles
7

Manongga, D. H. F. "Using genetic algorithm-based methods for financial analysis." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320950.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sun, Chi. "A constrained MDP-based vertical handoff decision algorithm for wireless networks." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1243.

Full text
Abstract:
The 4th generation wireless communication systems aim to provide users with the convenience of seamless roaming among heterogeneous wireless access networks. To achieve this goal, the support of vertical handoff is important in mobility management. This thesis focuses on the vertical handoff decision algorithm, which determines the criteria under which vertical handoff should be performed. The problem is formulated as a constrained Markov decision process. The objective is to maximize the expected total reward of a connection subject to the expected total access cost constraint. In our model, a benefit function is used to assess the quality of the connection, and a penalty function is used to model the signaling incurred and call dropping. The user's velocity and location information are also considered when making the handoff decisions. The policy iteration and Q-learning algorithms are employed to determine the optimal policy. Structural results on the optimal vertical handoff policy are derived by using the concept of supermodularity. We show that the optimal policy is a threshold policy in bandwidth, delay, and velocity. Numerical results show that our proposed vertical handoff decision algorithm outperforms other decision schemes in a wide range of conditions such as variations on connection duration, user's velocity, user's budget, traffic type, signaling cost, and monetary access cost.
APA, Harvard, Vancouver, ISO, and other styles
9

Nkansah-Gyekye, Yaw. "An intelligent vertical handoff decision algorithm in next generation wireless networks." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2726_1307443785.

Full text
Abstract:

The objective of the thesis research is to design such vertical handoff decision algorithms in order for mobile field workers and other mobile users equipped with contemporary multimode mobile devices to communicate seamlessly in the NGWN. In order to tackle this research objective, we used fuzzy logic and fuzzy inference systems to design a suitable handoff initiation algorithm that can handle imprecision and uncertainties in data and process multiple vertical handoff initiation parameters (criteria)
used the fuzzy multiple attributes decision making method and context awareness to design a suitable access network selection function that can handle a tradeoff among many handoff metrics including quality of service requirements (such as network conditions and system performance), mobile terminal conditions, power requirements, application types, user preferences, and a price model
used genetic algorithms and simulated annealing to optimise the access network selection function in order to dynamically select the optimal available access network for handoff
and we focused in particular on an interesting use case: vertical handoff decision between mobile WiMAX and UMTS access networks. The implementation of our handoff decision algorithm will provide a network selection mechanism to help mobile users select the best wireless access network among all available wireless access networks, that is, one that provides always best connected services to users.

APA, Harvard, Vancouver, ISO, and other styles
10

Ahmed, Mansoor, and Ali Murtaza. "Decision algorithm and procedure for fast handover between 3G and WLAN." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-5146.

Full text
Abstract:

Different types of wireless network systems have been developed to offer Internet access to mobile end users. Now days a burning issue is to provide the network facility to end users on anywhere and anytime bases. Typical examples of wireless networks are 3G and WLAN (wireless local area network). An important issue is to integrate these heterogeneous networks and manage the mobile nodes while moving across heterogeneous networks with session continuity, low latency for handover between networks that are based on different technologies (vertical handover) and minimum packet loss. To achieve this, it is important to find the right point in time to perform a handover between two networks and to find a new network that, in fact, improves the connectivity over a reasonable time span. In this thesis, we propose vertical handover techniques for mobile users, based on predictive and adaptive schemes for the selection of the next network. The handover decision between the technologies takes the velocity of the mobile node, battery condition of mobile node, signal to interference ratio (SIR), application requirements and received signal strength RSS) as decision parameters. In order to reduce unnecessary handover, the concept of dwell timer is used and to reduce the handover latency, the predictive mode of Fast Mobile IPv6 (FMIPv6) and for minimum packet loss, the concept of tunnelling and buffering of packets on new access router in advance are proposed.

APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Decision algorithm"

1

Koukoudakis, Alexandros. Visualisation decision algorithm for temporal database management system. Manchester: UMIST, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Margolis, Carmi Z. Solving common pediatric problems: An algorithm approach. New York: Solomon Press, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Stergiopoulos, D. G. A recursive soft-decision decoding algorithm for hamming codes. Manchester: UMIST, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2nd ed. London: Springer London, 2013.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Perny, Patrice, Marc Pirlot, and Alexis Tsoukiàs, eds. Algorithmic Decision Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41575-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Pekeč, Saša, and Kristen Brent Venable, eds. Algorithmic Decision Theory. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31489-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Rossi, Francesca, and Alexis Tsoukias, eds. Algorithmic Decision Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04428-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Brafman, Ronen I., Fred S. Roberts, and Alexis Tsoukiàs, eds. Algorithmic Decision Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24873-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Rothe, Jörg, ed. Algorithmic Decision Theory. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67504-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Walsh, Toby, ed. Algorithmic Decision Theory. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23114-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Decision algorithm"

1

Ming, Huang, Ji Baohui, and Liang Xu. "An Improved Bee Algorithm-Genetic Algorithm." In Intelligent Decision Technologies, 683–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22194-1_67.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Pannu, Arslan, and Ahmad Mirza. "Preoperative Decision-Making Algorithm." In Choledocholithiasis, 49–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74503-9_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Khazaii, Javad. "Genetic Algorithm Optimization." In Advanced Decision Making for HVAC Engineers, 87–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33328-1_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kiang, Tony K. L., Kyle John Wilby, and Mary H. H. Ensom. "Conclusion and Clinical Decision Algorithm." In Pharmacokinetic and Pharmacodynamic Drug Interactions Associated with Antiretroviral Drugs, 133–35. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2113-8_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Jian-hui, Liu, and Han Chang-jun. "A RSSI-Based Localization Algorithm in Smart Space." In Intelligent Decision Technologies, 671–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22194-1_66.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Calvete, Herminia I., Carmen Galé, and José A. Iranzo. "An Evolutionary Algorithm for the Biobjective Capacitated m-Ring Star Problem." In Algorithmic Decision Theory, 116–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41575-3_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zuo, Yi, Masaaki Harada, Takao Mizuno, and Eisuke Kita. "Bayesian Network Based Prediction Algorithm of Stock Price Return." In Intelligent Decision Technologies, 397–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29977-3_40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ouertani, Mohamed Wajdi, Ghaith Manita, and Ouajdi Korbaa. "Improved Genetic Algorithm for Electric Vehicle Charging Station Placement." In Intelligent Decision Technologies, 37–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2765-1_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Boryczka, Urszula, and Mariusz Boryczka. "Ant Clustering Algorithm with Information Theoretic Learning." In Intelligent Decision Technologies 2016, 325–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39630-9_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jedrzejowicz, Joanna, and Magdalena Zakrzewska. "Text Classification Using LDA-W2V Hybrid Algorithm." In Intelligent Decision Technologies 2019, 227–37. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8311-3_20.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Decision algorithm"

1

Sui, Yanan, and Joel W. Burdick. "Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/389.

Full text
Abstract:
We consider sequential decision making under uncertainty, the optimization over large decision space with noisy comparative feedback. This problem can be formulated as a K-armed Dueling Bandits problem where K is the total number of decisions. When K is very large, existing dueling bandits algorithms suffer huge cumulative regret before converging on the optimal arm. This paper studies the dueling bandits problem with a large number of dependent arms. Our problem is motivated by a clinical decision making process in large decision space. We propose an efficient algorithm CorrDuel for the problem which makes decisions to simultaneously deliver effective therapy and explore the decision space. Many sequential decision making problems with large and structured decision space could be facilitated by our algorithm. After evaluated the fast convergence of CorrDuel in analysis and simulation experiments, we applied it on a live clinical trial of therapeutic spinal cord stimulation. It is the first applied algorithm towards spinal cord injury treatments and experimental results show the effectiveness and efficiency of our algorithm.
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Run-Zong, Bin Fang, and Hui-Wu Luo. "Automatic decision support by rule exhaustion decision tree algorithm." In 2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2016. http://dx.doi.org/10.1109/icwapr.2016.7731623.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Liu, Run Zong, Yuan Yan Tang, and Bin Fang. "Automatic decision support by information energy decision tree algorithm." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974566.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kureichik, Vladimir, Elmar Kuliev, Dmitry Zaporozhets, and Yury Kravchenko. "Combined Algorithm for Decision Making." In 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2017. http://dx.doi.org/10.1109/icaict.2017.8687025.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kamal, Joarder Mohammad Mustafa, Md Junaebur Rashid, Tajul Islam, and Hasan Sarwar. "An intelligent handoff decision algorithm." In 2008 International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2008. http://dx.doi.org/10.1109/iccce.2008.4580564.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Sanchez, Daniel Prado, Marcos A. Pertierra, Erik Hemberg, and Una-May O'Reilly. "Competitive coevolutionary algorithm decision support." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3205784.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Fu, Yanglie, Mingliang Hou, and Yuran Liu. "Fractional-Order Association Decision Algorithm." In 2013 Fifth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2013. http://dx.doi.org/10.1109/iccis.2013.231.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ma, Ning, and Jianhe Guan. "Research on AHP decision algorithms based on BP algorithm." In 2ND INTERNATIONAL CONFERENCE ON MATERIALS SCIENCE, RESOURCE AND ENVIRONMENTAL ENGINEERING (MSREE 2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5005288.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Ying, and Clarence W. de Silva. "A Modified Q-Learning Algorithm for Multi-Robot Decision Making." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41643.

Full text
Abstract:
This paper presents a modified distributed Q-learning algorithm termed the Sequential Q-learning algorithm with Kalman Filtering (SQKF), for multi-robot decision making. While Q-learning is employed commonly in the multi-robot domain to support robot operation in dynamic and unknown environments, it also faces many challenges. It is questionable to scale the conventional single-agent Q-learning algorithm into the multi-robot domain because such an extension violates the Markov assumption on which the algorithm is based on. The empirical results show that it can confuse the robots and render them unable to learn a good cooperative policy due to incorrect credit assignment among robots and also make a robot incapable of observing the actions of other robots in the same environment. In this paper, a modified Q-learning algorithm termed the Sequential Q-learning Algorithm with Kalman Filtering (SQKF), which is suitable for multi-robot decision-making, is developed. The basic characteristics of the SQKF algorithm are: (1) the learning process is not parallel but sequential, i.e. the robots will not make decisions simultaneously and instead, they will learn and make decisions according to a predefined sequence; (2) a robot will not update its Q values with observed global rewards and instead, it will employ a specific Kalman filter to extract its real local reward from the global reward thereby updating its Q-table with this local reward. The new SQKF algorithm is intended to solve two problems in multi-robot Q-learning: Credit assignment and Behavior conflicts. The detailed procedure of the SQKF algorithm is presented and its application is illustrated. Empirical results show that the algorithm has better performance than the conventional single-agent Q-learning algorithm or the Team Q-learning algorithm in the multi-robot domain.
APA, Harvard, Vancouver, ISO, and other styles
10

Yikun, Zhang, Chen Enhui, Liang Junli, Hei Xinhong, and Xia Hui. "Simplified Branch Marking Algorithm Based on Decision-to-Decision Graph." In 2009 International Conference on New Trends in Information and Service Science (NISS). IEEE, 2009. http://dx.doi.org/10.1109/niss.2009.165.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Decision algorithm"

1

SIVAKS, ANNA, OKSANA GORBUNOVA, and YEVGENY FROLOV. ALGORITHM FOR MAKING A STRATEGIC DECISION ABOUT OUTSOURCING. Science and Innovation Center Publishing House, 2020. http://dx.doi.org/10.12731/2070-7568-2020-1-3-126-136.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chang, Hyeong S., Michael C. Fu, and Steven I. Marcus. An Adaptive Sampling Algorithm for Solving Markov Decision Processes. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada438505.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kostrzewski, Andrew, and Jeongdal Kim. A Highly Functional Decision Paradigm Based on Nonlinear Adaptive Genetic Algorithm. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada344278.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kim, Dai H. A Highly Functional Decision Paradigm Based on Nonlinear Adaptive Genetic Algorithm. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada281457.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Bertsekas, Dimitri P. Algorithms for Learning and Decision Making. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada591909.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ludwig, Jens, and Sendhil Mullainathan. Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System. Cambridge, MA: National Bureau of Economic Research, September 2021. http://dx.doi.org/10.3386/w29267.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kohavi, Ronny, and Nils J. Nilsson. Final Technical Report for Hybrid Algorithms and Oblivious Decision Graphs Using MLC++. Fort Belvoir, VA: Defense Technical Information Center, February 1996. http://dx.doi.org/10.21236/ada327585.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Borkman, Steve, Marlo Verdesca, and Jon Watkins. Algorithms for Ground Soldier Based Simulations and Decision Support Applications. Phase 1. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada560713.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Buchanan, Ben. The AI Triad and What It Means for National Security Strategy. Center for Security and Emerging Technology, August 2020. http://dx.doi.org/10.51593/20200021.

Full text
Abstract:
One sentence summarizes the complexities of modern artificial intelligence: Machine learning systems use computing power to execute algorithms that learn from data. This AI triad of computing power, algorithms, and data offers a framework for decision-making in national security policy.
APA, Harvard, Vancouver, ISO, and other styles
10

Zilberstein, Shlomo. New Algorithms for Collaborative and Adversarial Decision Making in Partially Observable Stochastic Games. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada495149.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Decision algorithm' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography