Journal articles: 'Classical learning'

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Khan, Tariq M., and Antonio Robles-Kelly. "Machine Learning: Quantum vs Classical." IEEE Access 8 (2020): 219275–94. http://dx.doi.org/10.1109/access.2020.3041719.

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Williams, Ben A. "Rejuvenation of Classical Learning Theory." Contemporary Psychology: A Journal of Reviews 37, no. 8 (August 1992): 772–73. http://dx.doi.org/10.1037/032447.

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Song Pyung-Nynul. "Application of Collaborative Learning in Teaching-Learning Classical Chinese." DONG-BANG KOREAN CHINESE LIEARATURE ll, no. 32 (June 2007): 401–23. http://dx.doi.org/10.17293/dbkcls.2007..32.401.

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Wang, R., L. J. Cong, and C. Yu. "The classical TDT perceptual learning is mostly temporal learning." Journal of Vision 13, no. 5 (April 12, 2013): 9. http://dx.doi.org/10.1167/13.5.9.

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CD, Diyana. "Improving Science Learning Outcomes About Material Through the Problem Based Learning Learning Model in Online Learning." Social, Humanities, and Educational Studies (SHEs): Conference Series 3, no. 4 (July 17, 2021): 429. http://dx.doi.org/10.20961/shes.v3i4.53380.

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<em>This research was carried out with the aim of improving student learning outcomes in science subjects about material using the Problem Based Learning model for fifth grade students at SD Negeri Limbangan</em><em> 01, Losari District, Brebes Regency, for the 2019/2020 academic year with a total of 11 students.The research shows an increase in the percentage of learning outcomes, this can be seen from the KKM completeness of students from pre-cycle activities, first cycles, and second cycles. The average value of students in the initial conditions (pre-cycle) is 53.64 with classical completeness of 45%. Cycle I was 67.27 with classical completeness of 73%. Cycle II was 83.64 with 91% classical completeness. Thus, it can be concluded that the use of Problem Based Learning (PBL) in online learning can improve student learning outcomes in learning science about material</em>

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Mauliandari, Riana, Made Sumarwati, and Arif Setyo Upoyo. "Peer Learning: An Effective Teaching-Learning Method for Improving Ability in Arterial Blood Gases Interpretation." Nurse Media Journal of Nursing 10, no. 3 (December 21, 2020): 329–38. http://dx.doi.org/10.14710/nmjn.v10i3.28660.

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Background: Competent nurses are expected to be able to interpret arterial blood gases (ABGs). The benefits of peer learning, an innovative teaching-learning method today, have long been recognized. However, to date, no studies have compared the effect of this method and the traditional classical method in interpreting ABGs. Purpose: This study aimed to compare the effect of peer learning and classical learning methods on the nurses’ ability to interpret ABGs. Method: This was a quasi-experimental research with pre and post-test design. Forty ward nurses were invited in the peer learning method group, and another 40 ward nurses were invited in the classical learning method group through a randomization process. Data were collected using a questionnaire before and after the educational intervention. The classical class was taught by an experienced trainer, while peer groups, divided into groups of 5-6, were taught by one member of each group who obtained the best pre-test score and received special training first. The analysis of data was performed by t-test.Result: The result showed that after the intervention, the mean score of interpreting ABGs in the peer learning group increased by 3.18±1.12 (p<0.001), while in the classical learning method, it only increased by 2.32±0.988 (p<0.001). Although there were significant increases in ABGs analysis’s ability in both groups, the peer teaching-learning group demonstrated a significantly greater improvement in interpreting ABGs (p<0.001).Conclusion: The peer learning method facilitates a more significant improvement in the nurses’ ability for ABGs interpretation. Peer learning is appropriate as one of the methods in clinical education for nurses.

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Ciliberto, Carlo, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, and Leonard Wossnig. "Quantum machine learning: a classical perspective." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2209 (January 2018): 20170551. http://dx.doi.org/10.1098/rspa.2017.0551.

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Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

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Shrivastava, Prakhar, Kapil Kumar Soni, and Akhtar Rasool. "Classical Equivalent Quantum Unsupervised Learning Algorithms." Procedia Computer Science 167 (2020): 1849–60. http://dx.doi.org/10.1016/j.procs.2020.03.204.

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Rihll, T. E. "Teaching and Learning in Classical Athens." Greece and Rome 50, no. 2 (October 2003): 168–90. http://dx.doi.org/10.1093/gr/50.2.168.

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Schreurs, Bernard G., and Daniel L. Alkon. "Imaging learning and memory: Classical conditioning." Anatomical Record 265, no. 6 (2001): 257–73. http://dx.doi.org/10.1002/ar.10031.

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Susilaningsih, Endang, Nurkintan Aprilia, and Sri Hayati. "The Effectiveness of Teaching Materials Loaded with Blended Learning with a Scientific Approach on Buffer Solution Learning." Jurnal Inovasi Pendidikan Kimia 16, no. 1 (January 2, 2022): 59–64. http://dx.doi.org/10.15294/jipk.v16i1.33774.

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The 2013 scientific-based curriculum, teachers must facilitate students to gain knowledge or skills based on the scientific method. The research aims to analyze the effectiveness of teaching materials. This type of research development, teaching materials have valid content and media. Teaching materials are declared effective if there is an increase in students' classical mastery, an increase in the achievement of basic competencies, and students can understand macroscopic, microscopic and symbolic level representations. Quantitative descriptive research method, starting from the analysis of the quality of teaching materials and diagnostic test instruments Two Tier Multiple Choice (TTMC), application of teaching materials, application of questions, data analysis of test results, classical completeness, achievement of basic competencies, and analysis of representational understanding. The research subjects were students of class XI and XII MIPA. The results of the study were obtained from the results of the student's TTMC test on the buffer solution material that had used Teaching Materials. Data analysis was done classically. The results showed that there was an increase in classical completeness by 9%, the achievement of basic competence indicators increased by 8% and concept understanding increased by 6%. So that the teaching materials developed are effective to use.

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Nivelkar, Mukta, and S. G. Bhirud. "Modeling of Supervised Machine Learning using Mechanism of Quantum Computing." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2161/1/012023.

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Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.

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VASILYEVA, Valentina D., Raisa M. PETRUNEVA, and Elena D. BERISHEVA. "CLASSICAL DIDACTIC PRINCIPLES AND ELECTRONIC LEARNING ENVIRONMENTS." PRIMO ASPECTU, no. 1(45) (March 24, 2021): 72–80. http://dx.doi.org/10.35211/2500-2635-2021-1-45-72-80.

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The article discusses the didactic aspects of the use of distance learning tools on the example of the electronic information and educational environment (EIEE) of the Volgograd State Technical University (VolgSTU). It is shown that classical didactic principles are also applicable when using information and communication technologies.

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Gomoluch, Pawel, Dalal Alrajeh, Alessandra Russo, and Antonio Bucchiarone. "Learning Neural Search Policies for Classical Planning." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 522–30. http://dx.doi.org/10.1609/icaps.v30i1.6748.

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Heuristic forward search is currently the dominant paradigm in classical planning. Forward search algorithms typically rely on a single, relatively simple variation of best-first search and remain fixed throughout the process of solving a planning problem. Existing work combining multiple search techniques usually aims at supporting best-first search with an additional exploratory mechanism, triggered using a handcrafted criterion. A notable exception is very recent work which combines various search techniques using a trainable policy. That approach, however, is confined to a discrete action space comprising several fixed subroutines.In this paper, we introduce a parametrized search algorithm template which combines various search techniques within a single routine. The template's parameter space defines an infinite space of search algorithms, including, among others, BFS, local and random search. We then propose a neural architecture for designating the values of the search parameters given the state of the search. This enables expressing neural search policies that change the values of the parameters as the search progresses. The policies can be learned automatically, with the objective of maximizing the planner's performance on a given distribution of planning problems. We consider a training setting based on a stochastic optimization algorithm known as the cross-entropy method (CEM). Experimental evaluation of our approach shows that it is capable of finding effective distribution-specific search policies, outperforming the relevant baselines.

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Aineto, Diego, Sergio Jiménez, and Eva Onaindia. "Learning STRIPS Action Models with Classical Planning." Proceedings of the International Conference on Automated Planning and Scheduling 28 (June 15, 2018): 399–407. http://dx.doi.org/10.1609/icaps.v28i1.13870.

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This paper presents a novel approach for learning strips action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given strips action model, even if this model is not fully specified.

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Gomoluch, Paweł, Dalal Alrajeh, and Alessandra Russo. "Learning Classical Planning Strategies with Policy Gradient." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 637–45. http://dx.doi.org/10.1609/icaps.v29i1.3531.

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A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the approaches. This enables using policy gradient to learn search strategies tailored to a specific distributions of planning problems and a selected performance metric, e.g. the IPC score. We instantiate the framework by constructing a policy space consisting of five search approaches and a two-dimensional representation of the planner’s state. Then, we train the system on randomly generated problems from five IPC domains using three different performance metrics. Our experimental results show that the learner is able to discover domain-specific search strategies, improving the planner’s performance relative to the baselines of plain bestfirst search and a uniform policy.

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Fathia, Alya Shofi, Dias Ayu Miftakhul Jannah, Razif Hanani, Heru Prastyo, and Muhammad Fahmi. "Student Learning Ethics: Classical Islamic Literature Analysis." Jurnal Indonesia Sosial Teknologi 3, no. 4 (April 13, 2022): 521–28. http://dx.doi.org/10.36418/jist.v3i4.410.

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This article uses literature review through the elaboration of classical texts, and some of literary sources until became this article. It started when there was an ethical degradation that occurred between students and teachers. This article aims to be a guideline for students who are studying. This is felt to be quite important by taking into account the quality of graduates produced by the education units of primary, secondary, and even higher education which slightly decreases in quality when compared to past scholars. Also starting to reduce students' understanding of ethics towards science and themselves when studying. The study in this journal uses a method of study of literature. The study of literature is a way to solve problems by looking through previously produced sources of writing. In other languages, the term for the study of literature is also very familiar with the title library study. Whereas ethics is one of the factors that are believed to affect the level of ease of understanding a science. This condition is the background of writing this article. By reviewing and analyzing the texts of classical books and other literature sources, it was successfully stated that among the ethics that need to be applied to every student are to cleanse themselves of various heart diseases, having the right intentions, choose the right teacher and be ethical in front of the teacher, choose good friends, and earnest in seeking knowledge.

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Arunachalam, Srinivasan, Alex Bredariol Grilo, and Aarthi Sundaram. "Quantum Hardness of Learning Shallow Classical Circuits." SIAM Journal on Computing 50, no. 3 (January 2021): 972–1013. http://dx.doi.org/10.1137/20m1344202.

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Lokanan, Mark, and Susan Liu. "Predicting Fraud Victimization Using Classical Machine Learning." Entropy 23, no. 3 (March 3, 2021): 300. http://dx.doi.org/10.3390/e23030300.

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Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

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Huang, Hsin-Yuan. "Learning quantum states from their classical shadows." Nature Reviews Physics 4, no. 2 (January 10, 2022): 81. http://dx.doi.org/10.1038/s42254-021-00411-5.

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Miura, H., Y. Makino, and M. Yano. "3J1030 A learning rule of classical conditioning." Seibutsu Butsuri 42, supplement2 (2002): S178. http://dx.doi.org/10.2142/biophys.42.s178_2.

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Luber, B., P. Balsam, T. Nguyen, M. Gross, and S. H. Lisanby. "Classical conditioned learning using transcranial magnetic stimulation." Experimental Brain Research 183, no. 3 (July 17, 2007): 361–69. http://dx.doi.org/10.1007/s00221-007-1052-7.

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de la Cruz, Joseph Sun, Dana Kulić, and William Owen. "A Comparison of Classical and Learning Controllers." IFAC Proceedings Volumes 44, no. 1 (January 2011): 1102–7. http://dx.doi.org/10.3182/20110828-6-it-1002.03279.

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Gao, X., Z. Y. Zhang, and L. M. Duan. "A quantum machine learning algorithm based on generative models." Science Advances 4, no. 12 (December 2018): eaat9004. http://dx.doi.org/10.1126/sciadv.aat9004.

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Quantum computing and artificial intelligence, combined together, may revolutionize future technologies. A significant school of thought regarding artificial intelligence is based on generative models. Here, we propose a general quantum algorithm for machine learning based on a quantum generative model. We prove that our proposed model is more capable of representing probability distributions compared with classical generative models and has exponential speedup in learning and inference at least for some instances if a quantum computer cannot be efficiently simulated classically. Our result opens a new direction for quantum machine learning and offers a remarkable example where a quantum algorithm shows exponential improvement over classical algorithms in an important application field.

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Azzajjad, Muhammad Fath, Miswandi Tendrita, and Dewi Satria Ahmar. "Effect of animation and review video making (arvima) in non-classical learning model on independent learning and students’ learning outcome." Linguistics and Culture Review 5, S3 (November 3, 2021): 967–76. http://dx.doi.org/10.21744/lingcure.v5ns3.1657.

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The non-classical learning model used in this study was a jigsaw type cooperative learning model with the assignment treatment of making learning material video which is expected to make it easier in finding knowledge in teaching materials, with the creativity of students’ forming skills. The purpose of this study are to determine: (a) the effect of animation video and review video making in non-classical learning model on the ability to learn independently of students of the chemistry education study program at USN Kolaka, (b) the effect of animation video and review video making in non-classical learning model on the ability of spatial independence of students in the chemistry education study program at USN Kolaka, and (c) the effect of animation video and review video making in non-classical learning model on the learning outcome of students in the chemistry education study program at USN Kolaka. This research was a quasi-experimental research (quasy experiment) with a posttest only research design. The research population was students in the IV and VI semester of Chemistry Education Study Program. The instrument used was a questionnaire of learning independence, spatial ability and learning outcome.

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As, M. Yusuf. "Influence of STAD cooperative learning model to improving physics learning process and learning outcomes." Jurnal Pijar Mipa 16, no. 4 (September 4, 2021): 519–24. http://dx.doi.org/10.29303/jpm.v16i4.2346.

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Efforts to improve student learning processes and outcomes use the STAD type cooperative learning model with the following stages: (1) teaching, presenting lessons; (2) discussion, students work in their groups guided by activity sheets to complete the subject matter; (3) test, students take the quiz individually; and (4) group rewards. The data collected in this study were in the form of activity observation data, questionnaires, and learning outcomes tests. The results showed that the students' activities were in the very active category in cycle 1, very active in cycle 2, and the very active category in cycle 3. The teacher's activities were in the very active category in the three cycles. The average cognitive aspect of students is 76 with classical completeness 74% in cycle 1, the average cognitive aspect of students is 75 with classical completeness 83% in cycle 2, and the average value of cognitive aspect is 82 with classical completeness 91 % in cycle 3. The results of the ANOVA analysis in the treatment of each cycle showed a significant effect with a value of 0.01 below 0.05. Based on this data, it can be concluded that the application of the STAD type cooperative learning model can improve the process and learning outcomes of class X in the Physics lesson of SMKN 1 Narmada.

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Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum deep learning." Quantum Information and Computation 16, no. 7&8 (May 2016): 541–87. http://dx.doi.org/10.26421/qic16.7-8-1.

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In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of multilayer and fully connected models.

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Alagic, Gorjan, Stacey Jeffery, Maris Ozols, and Alexander Poremba. "On Quantum Chosen-Ciphertext Attacks and Learning with Errors." Cryptography 4, no. 1 (March 21, 2020): 10. http://dx.doi.org/10.3390/cryptography4010010.

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Large-scale quantum computing poses a major threat to classical public-key cryptography. Recently, strong “quantum access” security models have shown that numerous symmetric-key cryptosystems are also vulnerable. In this paper, we consider classical encryption in a model that grants the adversary quantum oracle access to encryption and decryption, but where we restrict the latter to non-adaptive (i.e., pre-challenge) queries only. We formalize this model using appropriate notions of ciphertext indistinguishability and semantic security (which are equivalent by standard arguments) and call it QCCA 1 in analogy to the classical CCA 1 security model. We show that the standard pseudorandom function ( PRF )-based encryption schemes are QCCA 1 -secure when instantiated with quantum-secure primitives. Our security proofs use a strong bound on quantum random-access codes with shared randomness. Revisiting plain IND − CPA -secure Learning with Errors ( LWE ) encryption, we show that leaking only a single quantum decryption query (and no other leakage or queries of any kind) allows the adversary to recover the full secret key with constant success probability. Information-theoretically, full recovery of the key in the classical setting requires at least a linear number of decryption queries. Our results thus challenge the notion that LWE is unconditionally “just as secure” quantumly as it is classically. The algorithm at the core of our attack is a new variant of the well-known Bernstein–Vazirani algorithm. Finally, we emphasize that our results should not be interpreted as a weakness of these cryptosystems in their stated security setting (i.e., post-quantum chosen-plaintext secrecy). Rather, our results mean that, if these cryptosystems are exposed to chosen-ciphertext attacks (e.g., as a result of deployment in an inappropriate real-world setting) then quantum attacks are even more devastating than classical ones.

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Montare, Alberto. "Classical Conditioning of Beginning Reading Responses." Perceptual and Motor Skills 67, no. 2 (October 1988): 611–21. http://dx.doi.org/10.2466/pms.1988.67.2.611.

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The present study describes the first demonstration that laboratory-controlled experimental procedures can lead to the successful acquisition and subsequent retention of classically conditioned beginning reading responses (CCBRRs) in children of both sexes and mean age of 4 yr. Anticipatory instructions combined with higher-order classical conditioning temporally arranged into a trace conditioning paradigm presented for 10 trials for each response to be learned led to beginning reading responses being successfully acquired by 20 children during 95% of the 2,220 total acquisition learning trials and subsequently correctly recalled on 114 of the 222 retention test trials. Findings support the view that perhaps the relatively sudden and sustained acquisition learning curves for reading responses on the second-signalling-system level of behavior in the present study may be quite different from the relatively slow and incremental learning curves usually obtained in classical conditioning of the autonomic type which occur on the first-signalling-system level.

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Juaini, Muhammad. "Model Kooperatif Tipe Teams Games Tournaments (TGT) pada Pembelajaran Materi Trigonometri untuk Meningkatkan Aktifitas dan Prestasi Belajar Siswa SMAN 3 Selong." Journal on Education 5, no. 2 (January 25, 2023): 4326–39. http://dx.doi.org/10.31004/joe.v5i2.1148.

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This study aims to determine the application of the TGT type learning model in increasing student activity and learning achievement on the subject matter of trigonometry in class X MIPA-3 SMAN 3 Selong. with 36 students. This type of research is Classroom Action Research which consists of planning, implementing, observing, evaluating and reflecting. The results showed that students were more active in the teaching and learning process and there was an increase in mathematics learning achievement of class X MIPA-3 students of SMAN 3 Selong by applying a TGT-type cooperative model to learning the subject mattertrigonometry. This increase in achievement is seen from the increase in the percentage of student learning completion classically in cycle I with classical completeness of 65.71%, cycle II with classical completeness of 76.47% and in cycle III with classical completeness of 90.09%. In addition, after implementing with the TGT-type cooperative model, student learning activities increase each cycle with active, active, and very active criteria. From the results of this study, it can be concluded that the application of the TGT type cooperative model to the learning of trigonometric subject matter can increase the activity and learning achievement of class X MIPA-3 students of SMAN 3 Selong.

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Ulizulfa, Annisa, Retno Kusumaningrum, Khadijah Khadijah, and Rismiyati Rismiyati. "Temperament detection based on Twitter data: classical machine learning versus deep learning." International Journal of Advances in Intelligent Informatics 8, no. 1 (March 31, 2022): 45. http://dx.doi.org/10.26555/ijain.v8i1.692.

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Deep learning has shown promising results in various text-based classification tasks. However, deep learning performance is affected by the number of data, i.e., when the number of data is small, deep learning algorithms do not perform well, and vice versa. Classical machine learning algorithms commonly work well for a few data, and their performance reaches an optimal value and does not increase with the increase in sample data. Therefore, this study aimed to compare the performance of classical machine learning and deep learning methods to detect temperament based on Indonesian Twitter. In this study, the proposed Indonesian Linguistic Inquiry and Word Count were employed to analyze the context of Twitter. The classical machine learning methods implemented were support vector machine and K-nearest neighbor, whereas the deep learning method employed was a convolutional neural network (CNN) with three different architectures. Both learning methods were implemented using multiclass classification and one versus all (OVA) multiclass classification. The highest average f-measure was 58.73%, obtained by CNN OVA with a pool size of 3, a dropout value of 0.7, and a learning rate value of 0.0007.

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Stockhorst, Ursula. "Role of classical conditioning in learning gastrointestinal symptoms." World Journal of Gastroenterology 13, no. 25 (2007): 3430. http://dx.doi.org/10.3748/wjg.v13.i25.3430.

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Moll, Maximilian, and Leonhard Kunczik. "Comparing quantum hybrid reinforcement learning to classical methods." Human-Intelligent Systems Integration 3, no. 1 (March 2021): 15–23. http://dx.doi.org/10.1007/s42454-021-00025-3.

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AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.

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Rozos, Evangelos, Demetris Koutsoyiannis, and Alberto Montanari. "KNN vs. Bluecat—Machine Learning vs. Classical Statistics." Hydrology 9, no. 6 (June 6, 2022): 101. http://dx.doi.org/10.3390/hydrology9060101.

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Uncertainty is inherent in the modelling of any physical processes. Regarding hydrological modelling, the uncertainty has multiple sources including the measurement errors of the stresses (the model inputs), the measurement errors of the hydrological process of interest (the observations against which the model is calibrated), the model limitations, etc. The typical techniques to assess this uncertainty (e.g., Monte Carlo simulation) are computationally expensive and require specific preparations for each individual application (e.g., selection of appropriate probability distribution). Recently, data-driven methods have been suggested that attempt to estimate the uncertainty of a model simulation based exclusively on the available data. In this study, two data-driven methods were employed, one based on machine learning techniques, and one based on statistical approaches. These methods were tested in two real-world case studies to obtain conclusions regarding their reliability. Furthermore, the flexibility of the machine learning method allowed assessing more complex sampling schemes for the data-driven estimation of the uncertainty. The anatomisation of the algorithmic background of the two methods revealed similarities between them, with the background of the statistical method being more theoretically robust. Nevertheless, the results from the case studies indicated that both methods perform equivalently well. For this reason, data-driven methods can become a valuable tool for practitioners.

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Tatontos, Diandra Sutria. "TROUBLESHOOTING STUDENTS' SLOW LEARNING THROUGH CLASSICAL TUTORING SERVICE." Educouns Journal: Jurnal Pendidikan dan Bimbingan Konseling 1, no. 2 (November 30, 2020): 58–64. http://dx.doi.org/10.53682/educouns.v1i2.699.

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Solving the problem of learning difficulties of students who are slow to learn through guidance services is a form of guidance and counseling services that autonomy students in teaching and learning activities. This research was conducted using the action research method where there are two research cycles. The number of respondents was 20 junior high school students at Minahasa District. The research results showed that the learning problems experienced by students could be overcome

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Patel, Monalisa, John B. Tan, and Fu-Sheng Chou. "Non-Linear Algorithms in Supervised Classical Machine Learning." Neonatology Today 16, no. 7 (July 20, 2021): 40–43. http://dx.doi.org/10.51362/neonatology.today/202171674043.

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Bakhtiyarovna, Najmutdinova Gulnoza. "The classical approach to learning a foreign language." Asian Journal of Multidimensional Research 10, no. 12 (2021): 323–27. http://dx.doi.org/10.5958/2278-4853.2021.01228.3.

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Alkaoud, Mohamed, and Mairaj Syed. "Learning to Identify Narrators in Classical Arabic Texts." Procedia Computer Science 189 (2021): 335–42. http://dx.doi.org/10.1016/j.procs.2021.05.109.

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Aoki, Michiko Y., and Felicia G. Bock. "Classical Learning and Taoist Practices in Early Japan." Monumenta Nipponica 41, no. 1 (1986): 107. http://dx.doi.org/10.2307/2384781.

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InYoung Lee. "On Japanese language education and learning classical Japanese." Journal of Japanese Studies ll, no. 63 (March 2015): 439–55. http://dx.doi.org/10.15733/jast.2015..63.439.

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Kappen, H. J. "Learning quantum models from quantum or classical data." Journal of Physics A: Mathematical and Theoretical 53, no. 21 (May 12, 2020): 214001. http://dx.doi.org/10.1088/1751-8121/ab7df6.

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Baharuddin, Baharuddin, and Suyadi Suyadi. "Implementation of The Classical Conditioning in PAI Learning." IJECA (International Journal of Education and Curriculum Application) 3, no. 1 (April 9, 2020): 7. http://dx.doi.org/10.31764/ijeca.v3i1.2024.

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SMAN 1 Teladan in the study of Islamic education uses the behavioristic theory known as classical conditioning. This research aims to determine the implementation of classical conditioning in the study of Islamic education in SMAN 1 Teladan and the factors that influence it. The study used qualitative research with a case study approach. Data collection is conducted using observation methods and interviews. The results showed that the application of classical conditioning in PAI learning was done through several stages, namely (1) making the classroom atmosphere comfortable. The stage is to give students the freedom to express their opinions during the discussion, slip intermezo in the material explanation, sometimes using English in material explanations, and the use of LCD in the Learning. (2) assigning continuous tasks. Assignments are given in the form of group discussions and Kultum. Students perform the Kultum when the teacher prepares the teaching materials. In addition, the stimulus makes students active in learning. The factors that influence it are factors of the teacher, teachers have the ability to understand the psychology of students so that he understands the theory or the right method to use. Student factors, students have a sense of responsibility for a given assignment, and complementary attitudes in a cult assignment. Institutional factors, institutions or schools that support in terms of vision and mission and school policies.

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SHI, Yi, and HaiPeng LI. "Population genomics: From classical statistics to supervised learning." SCIENTIA SINICA Vitae 49, no. 4 (March 21, 2019): 445–55. http://dx.doi.org/10.1360/n052018-00207.

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Zipser, David. "A model of hippocampal learning during classical conditioning." Behavioral Neuroscience 100, no. 5 (1986): 764–76. http://dx.doi.org/10.1037/0735-7044.100.5.764.

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Westland, James Christopher, and Manfred Kochen. "Classical conditioning: A manifestation of Bayesian neural learning." Behavioral and Brain Sciences 12, no. 1 (March 1989): 160. http://dx.doi.org/10.1017/s0140525x00024857.

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Després, Jean-Philippe, Pamela Burnard, Francis Dubé, and Sophie Stévance. "Expert improvisers in Western classical music learning pathways." Thinking Skills and Creativity 22 (December 2016): 167–79. http://dx.doi.org/10.1016/j.tsc.2016.10.006.

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Crocco, Laura, Patricia McCabe, and Catherine Madill. "Principles of Motor Learning in Classical Singing Teaching." Journal of Voice 34, no. 4 (July 2020): 567–81. http://dx.doi.org/10.1016/j.jvoice.2018.12.019.

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Westelius, Alf. "On Classical Music and Business—Listening, Leading, Learning." Reflections: The SoL Journal 2, no. 4 (June 1, 2001): 47–57. http://dx.doi.org/10.1162/152417301750385459.

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Mari, Andrea, Thomas R. Bromley, Josh Izaac, Maria Schuld, and Nathan Killoran. "Transfer learning in hybrid classical-quantum neural networks." Quantum 4 (October 9, 2020): 340. http://dx.doi.org/10.22331/q-2020-10-09-340.

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We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained classical network is modified and augmented by a final variational quantum circuit. This approach is particularly attractive in the current era of intermediate-scale quantum technology since it allows to optimally pre-process high dimensional data (e.g., images) with any state-of-the-art classical network and to embed a select set of highly informative features into a quantum processor. We present several proof-of-concept examples of the convenient application of quantum transfer learning for image recognition and quantum state classification. We use the cross-platform software library PennyLane to experimentally test a high-resolution image classifier with two different quantum computers, respectively provided by IBM and Rigetti.

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Mamentu, Meike D. "Online Class Resitation Learning Model During the Covid-19 Pandemic to Increase Outcomes Students Learning in Class Accounting Lessons." International Educational Research 4, no. 2 (January 21, 2022): p40. http://dx.doi.org/10.30560/ier.v4n2p40.

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This study aims to determine the improvement of student learning outcomes through the online class recitation learning model during the covid-19 pandemic, on the learning process of class X1 students of the Accounting Department at SMK Negeri 1 Tompaso Baru Minahasa, totaling 14 students. The online classroom research method used is in the form of CAR (Classroom Action Research) which refers to classroom action research. In terms of treatment, learning is given that applies an online class recitation learning model. From the results of the analysis obtained by researchers on accounting subjects with service company accounting cycle material with the stages: Recording transactions/documents into general journals (General Entries) and posting from general journals to general ledgers (General Ledger), there is an increase in student learning outcomes. This increase can be seen from the percentage of student learning completeness individually and classically. This increase can be seen from the percentage of student learning completeness individually and classically. At the pre-cycle stage or before the implementation of the action, the average value of the class was 63.92 with the percentage of classical learning completeness only 35%. In the first cycle, it reached an average of 71.78 with a classical mastery level of 64.28%. While the learning outcomes in the second cycle reached an average of 77.85 with the classical learning completeness level reaching 92.85%. Likewise with student learning activities at every face-to-face online class. Student activity at the 1st meeting only reached a percentage of 56.25% with high criteria. At the second meeting it reached a percentage of 72.76% with high criteria, and at the third meeting it reached a percentage of 83.92% with very high criteria. Thus, student learning activities and student learning completeness in online classes during the COVID-19 pandemic increase by using the online class recitation learning model. With learning outcomes reaching 92.85% above the minimum learning completeness.

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Journal articles on the topic 'Classical learning'

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