The perceptron: A probabilistic model for information storage and organization in the brain.

A logical calculus of the ideas immanent in nervous activity

McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566

The Organization of Behavior

Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825

Representation of Events in Nerve Nets and Finite Automata

Kleene, S. (1951) · Princeton University Press eBooks · doi:10.1515/9781400882618-002 · Cited by 1,927

Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components

von Neumann, J. (1956) · Cited by 1,652

Untitled work

(1952) · Medical Entomology and Zoology · Cited by 1,436

The Sensory Order.

Kneale, M., & von Hayek, F. A. (1954) · The Philosophical Quarterly · doi:10.2307/2216396 · Cited by 494

Human Nature in the Light of Psychopathology

Goldstein, K. (1940) · Harvard University Press eBooks · doi:10.4159/harvard.9780674492103 · Cited by 286

The cell assembly: Mark II.

Milner, P. M. (1957) · Psychological Review · doi:10.1037/h0042287 · Cited by 235

Human Nature in the Light of Psychopathology.

L., H. A., & Goldstein, K. (1941) · The Journal of Philosophy · doi:10.2307/2017294 · Cited by 126

Why the Mind is in the Head?

McCulloch, W. S. (1950) · dialectica · doi:10.1111/j.1746-8361.1950.tb01019.x · Cited by 73

Force balance controls the relaxation time of the gradient descent algorithm in the satisfiable phase

Hwang, S., & Ikeda, H. (2020) · Physical review. E · doi:10.1103/physreve.101.052308 · Cited by 10

Introduction to Rare-Event Predictive Modeling for Inferential Statisticians—A Hands-On Application in the Prediction of Breakthrough Patents

Hain, D. S., & Jurowetzki, R. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-030-98689-6_5 · Cited by 10

Maximal Closed Set and Half-Space Separations in Finite Closure Systems

Seiffarth, F., Horváth, T., & Wrobel, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46150-8_2 · Cited by 10

An integrated classification model for incremental learning

Hu, J. Y., Yan, C. C., Liu, X., Li, Z., Ren, C., Zhang, J., Peng, D., & Yang, Y. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10070-w · Cited by 10

Deep Kernel machines: a survey

Nair, N. K., Afzal, A. L., & Asharaf, S. (2020) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00933-1 · Cited by 10

swFLOW: A large-scale distributed framework for deep learning on Sunway TaihuLight supercomputer

Li, M., Han, L., Chen, J., Diaz, J. M. M., Xiao, Q., Lin, R., Wang, F., Gao, G. R., & An, H. (2021) · Information Sciences · doi:10.1016/j.ins.2020.12.079 · Cited by 10

Process planning for die and mold machining based on pattern recognition and deep learning

Hashimoto, M., & NAKAMOTO, K. (2021) · Journal of Advanced Mechanical Design Systems and Manufacturing · doi:10.1299/jamdsm.2021jamdsm0015 · Cited by 10

A Survey of Supervised Learning Models for Spiking Neural Network

Agebure, M. A., Wumnaya, P. A., & Baagyere, E. Y. (2021) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2021/v9i430228 · Cited by 10

Intelligence artificielle : entre science et marché

Vayre, J.-S. (2021) · Annales des Mines - Gérer et comprendre · doi:10.3917/geco1.145.0055 · Cited by 10

Self-adaptive deep neural network: Numerical approximation to functions and PDEs

Cai, Z., Chen, J., & Liu, M. (2022) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111021 · Cited by 10

Support Vector Machine Applied to the Optimal Design of Composite Wing Panels

dos Santos, R. R., Machado, T. G. D. P., & Castro, S. G. P. (2021) · Aerospace · doi:10.3390/aerospace8110328 · Cited by 10

Optimizing single-photon generation and storage with machine learning

Cai, M., Lu, Y., Xiao, M., & Xia, K. (2021) · Physical Review A · doi:10.1103/physreva.104.053707 · Cited by 10

A Comprehensive Taxonomy of Dynamic Texture Representation

Nguyễn, T. P., & Nguyen, T. T. (2021) · ACM Computing Surveys · doi:10.1145/3487892 · Cited by 10

Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents

Loechte, A., Ruiz, I. R., & Glösekötter, P. (2021) · Applied Sciences · doi:10.3390/app12010274 · Cited by 10

Graph Neural Networks in Data Mining

Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184.017 · Cited by 10

Auditory model-based estimation of the effect of head-worn devices on frontal horizontal localisation

Lladó, P., Hyvärinen, P., & Pulkki, V. (2022) · Acta Acustica · doi:10.1051/aacus/2021056 · Cited by 10

ROWBACK: RObust Watermarking for neural networks using BACKdoors

Chattopadhyay, N., & Chattopadhyay, A. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00274 · Cited by 10

Philosophy of Technology

Friis, J. K. B. O. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316779651.041 · Cited by 10

Methodologies and Applications of Artificial Intelligence in Systems Engineering

Ali, A. K., & Mahmoud, M. S. (2022) · International Journal of Robotics and Control Systems · doi:10.31763/ijrcs.v2i1.532 · Cited by 10

A Novel ASIC Design Flow Using Weight-Tunable Binary Neurons as Standard Cells

Wagle, A., Singh, G., Khatri, S. P., & Vrudhula, S. B. K. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3164995 · Cited by 10

Self-organization and Emergence of Dynamical Structures in Neuromorphic Atomic Switch Networks

Stieg, A. Z., Avizienis, A. V., Sillin, H. O., Aguilera, R., Shieh, H.-H., Martin‐Olmos, C., Sandouk, E. J., Aono, M., & Gimzewski, J. K. (2019) · Springer eBooks · doi:10.1007/978-3-319-76375-0_14 · Cited by 10

Developing Intelligent Robots that Grasp Affordance

Loeb, G. E. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.951293 · Cited by 10

Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection

Manzini, N., Orcesi, A., Thom, C., Brossault, M.-A., Botton, S., Ortiz, M., & Dumoulin, J. (2022) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0003469 · Cited by 10

A comparison of machine learning methods to predict rheometric properties of rubber compounds

Uruk, Z., Kiraz, A., & Deniz, V. (2022) · Journal of Rubber Research · doi:10.1007/s42464-022-00170-7 · Cited by 10

A Brief History and Foundations for Modern Artificial Intelligence

Luger, G. F. (2022) · International Journal of Semantic Computing · doi:10.1142/s1793351x22500076 · Cited by 10

Predictive Analytics Applications for Small and Medium-Sized Enterprises (SMEs)—A Mini Survey and Real-World Use Cases

Bøgh, S., Hain, D. S., Hansen, E. B., Jensen, S. B., Tvedebrink, T., & Jurowetzki, R. (2022) · Springer eBooks · doi:10.1007/978-3-031-15428-7_22 · Cited by 10

Layer-Wise Relevance Propagation for Echo State Networks Applied to Earth System Variability

Landt-Hayen, M., Kröger, P., Claus, M., & Rath, W. (2022) · doi:10.5121/csit.2022.122008 · Cited by 10

Estimating divergent forest carbon stocks and sinks via a knife set approach

Wang, S., Kobayashi, K., Takanashi, S., Liu, C.-P., Li, D.-R., Chen, S.-W., Cheng, Y.-T., Moriguchi, K., & Dannoura, M. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.117114 · Cited by 10

Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control

Wijaya, S., Heryadi, Y., Arifin, Y., Suparta, W., & Lukas, L. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.129 · Cited by 10

Enterprise Digital Twins for Risk Free Business Experimentations

Barat, S., Kulkarni, V., & Bhattacharya, K. (2022) · Winter Simulation Conference · doi:10.1109/wsc57314.2022.10015412 · Cited by 10

The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines

Arsiwalla, X. D., Solé, R. V., Moulin-Frier, C., Herreros, I., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2023) · NeuroSci · doi:10.3390/neurosci4020009 · Cited by 10

Applications of Artificial Intelligence and Machine Learning in Geospatial Data

Srivastava, N., & Saxena, N. (2023) · Advances in geospatial technologies book series · doi:10.4018/978-1-6684-7319-1.ch010 · Cited by 10

Numerical analysis of large masonry structures: bridging meso and macro scales via artificial neural networks

Koocheki, K., & Pietruszczak, S. (2023) · Computers & Structures · doi:10.1016/j.compstruc.2023.107042 · Cited by 10

A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete

Prakash, S., Kumar, S., & Rai, B. (2023) · Asian Journal of Civil Engineering · doi:10.1007/s42107-023-00822-y · Cited by 10

Binary Classification with Imbalanced Data

Chiang, J., Lio, Y., Hsu, C.-Y., Ho, C., & Tsai, T. (2023) · Entropy · doi:10.3390/e26010015 · Cited by 10

METHOD COMPARISON IN THE DECISION SUPPORT SYSTEM OF A SCHOLARSHIP SELECTION

Bachtiar, M. I., Suyono, H., & Purnomo, M. F. E. (2022) · Jurnal Ilmiah KURSOR · doi:10.21107/kursor.v11i2.263 · Cited by 10

Examining the Application of Artificial Neural Networks (ANNs) for Advancing Energy Efficiency in Building: A Comprehensive Reviews

Alaoui, M. E., & Rougui, M. (2024) · Journal of Sustainability Research · doi:10.20900/jsr20240001 · Cited by 10

Support Vector Machines

Liu, Z. “. (2025) · Springer eBooks · doi:10.1007/978-3-031-75953-6_5 · Cited by 10

Robustness Analysis of Eleven Linear Classifiers in Extremely High–Dimensional Feature Spaces

Lausser, L. M., & Kestler, H. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12159-3_7 · Cited by 10

Preliminary Considerations for a Quantitative Theory of Networked Embodied Intelligence

Bonsignorio, F. (2007) · Springer eBooks · doi:10.1007/978-3-540-77296-5_11 · Cited by 10

Approximating Reflectance Functions using Neural Networks

Gargan, D., & Neelamkavil, F. (1998) · Eurographics · doi:10.1007/978-3-7091-6453-2_3 · Cited by 10

An efficient online active learning algorithm for binary classification

Liu, D., Zhang, P., & Zheng, Q. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.08.010 · Cited by 10

Assessing the Impact of Market-Focused and Price-Based Strategies on Performance: A Neural Network Typology

Phillips, P., Davies, F., & Moutinho, L. (2002) · Journal of Market-Focused Management · doi:10.1023/a:1022942806776 · Cited by 10

Neural Network Algorithms for the Correction of Concrete Slab Stresses from Linear Elastic Layered Programs

Haussmann, L. D., Tutumluer, E., & Barenberg, E. J. (1997) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1568-06 · Cited by 10

Feature Mapping the Seoul Metro Station Areas Based on a Self-Organizing Map

Sohn, K. (2013) · Journal of Urban Technology · doi:10.1080/10630732.2013.855514 · Cited by 10

Connectionism and artificial intelligence: history and philosophical interpretation

Aizawa, K. (1992) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/09528139208953753 · Cited by 10

Les réseaux de neurones et la régression multiple en prédiction de l’érosion spécifique: cas du bassin hydrographique Algérois-Hodna-Soummam (Algérie)

Salhi, C., Touaïbia, B., & Zeroual, A. (2013) · Hydrological Sciences Journal · doi:10.1080/02626667.2013.824090 · Cited by 10

Acoustic Modelling Using Continuous Rational Kernels

Layton, M., & Gales, M. (2007) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1007/s11265-006-0027-4 · Cited by 10

What is Soft Computing? Bridging Gaps for 21st Century Science!

Seising, R. (2010) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2010.9727687 · Cited by 10

Topology constraint free fuzzy gated neural networks for pattern recognition

Chandrasekaran, V., & Liu, Z. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.668890 · Cited by 10

SOLAR: Scalable Online Learning Algorithms for Ranking

Wang, J., Wan, J., Zhang, Y., & Hoi, S. C. H. (2015) · doi:10.3115/v1/p15-1163 · Cited by 10

Active online classification via information maximization

Slonim, N., Yom‐Tov, E., & Crammer, K. (2011) · International Joint Conference on Artificial Intelligence · doi:10.5591/978-1-57735-516-8/ijcai11-252 · Cited by 10

Online Feature Selection of Class Imbalance via PA Algorithm

Han, C., Tan, Y.-K., Zhu, J., Guo, Y. F., Chen, J., & Wu, Q. (2016) · Journal of Computer Science and Technology · doi:10.1007/s11390-016-1656-0 · Cited by 10

Artificial neural network for the configuration problem in solids

Ji, H., & Jung, Y. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4974928 · Cited by 10

Simulation of longitudinal surface settlement due to tunnelling using artificial neural network

Marto, A., Hajihassani, M., Kalatehjari, R., Namazi, E., & Sohaei, H. (2012) · International Review on Modelling and Simulations (IREMOS) · Cited by 10

Survey of Data Mining (DM) and Machine Learning (ML) Methods on Cyber Security

Shanbhogue, R. D., & Beena, B. M. (2017) · Indian Journal of Science and Technology · doi:10.17485/ijst/2017/v10i35/118951 · Cited by 10

Metodologia de baixo custo para mapeamento geotécnico aplicado à pavimentação

Ribeiro, A. J. A., da Silva, C. A. U., & Barroso, S. H. D. A. (2018) · Transportes · doi:10.14295/transportes.v26i2.1491 · Cited by 10

Symmetry Detection and Classification in Drawings of Graphs

Luca, F. D., Hossain, M. I., & Kobourov, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35802-0_38 · Cited by 10

Building automated survey coders via interactive machine learning

Moreo, A., Esuli, A., & Sebastiani, F. (2019) · International Journal of Market Research · doi:10.1177/1470785318824244 · Cited by 10

Neural network design for energy-autonomous artificial intelligence applications using temporal encoding

Mileiko, S., Bunnam, T., Xia, F., Shafik, R., Yakovlev, A., & Das, S. (2019) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2019.0166 · Cited by 10

Automatic Electron Density Determination by Using a Convolutional Neural Network

Hasegawa, T., Matsuda, S., Kumamoto, A., Tsuchiya, F., Kasahara, Y., Miyoshi, Y., Kasaba, Y., Matsuoka, A., & Shinohara, I. (2019) · IEEE Access · doi:10.1109/access.2019.2951916 · Cited by 10

A Multilayer Perceptron for Obtaining Quick Parameter Estimations of Cool Exoplanets from Geometric Albedo Spectra

Johnsen, T. K., Marley, M. S., & Gulick, V. C. (2020) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab740d · Cited by 10

Deep Learning's Governmentality

Roberge, J., Morin, K., & Senneville, M. (2019) · transcript Verlag eBooks · doi:10.14361/9783839447192-008 · Cited by 10

Performance Analysis of Convolutional Neural Network Using Multi-level Memristor Crossbar for Edge Computing

Cao, T., Goh, W. L., & Gao, Y. (2020) · Intelligent Autonomous Systems · doi:10.1109/icoias49312.2020.9081857 · Cited by 10

Hidroelektrik santrallarda bakım çizelgeleme için hibrid bir model önerisi

Özcan, E., DANIŞAN, T., & Eren, T. (2020) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.602774 · Cited by 10

Review of Concept Drift Detection Method for Industrial Process Modeling

Sun, Z., Tang, J., Qiao, J., & Cui, C. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9189106 · Cited by 10

Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization

Du, Y., Tan, Z., Qian, C., Zhang, Y., & Chongjun, W. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200208 · Cited by 10

GPS Continues Wave Jamming Canceller using an ANF Combined with an Artificial Neural Network

Abbasi, M., Mosavi, M. R., & Reazei, M. J. (2020) · Iranian Joint Congress on Fuzzy and Intelligent Systems · doi:10.1109/cfis49607.2020.9238700 · Cited by 10

Detection, Features Extraction and Classification of Radio-Frequency Pulses in a High-Voltage Power Substation: Results from a Measurement Campaign

Palo, M., Schubert, B., Ogliari, E., Wei, J., Gu, K., & Liu, W. (2020) · IEEE International Conference on Dielectrics (ICD) · doi:10.1109/icd46958.2020.9342022 · Cited by 10

Selective Neuron Re-Computation (SNRC) for Error-Tolerant Neural Networks

Liu, S., Reviriego, P., & Lombardi, F. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3056992 · Cited by 10

Monitoring and Application of Artificial Neural Network Model for Prediction of Organophosphorus Pesticides Residue in Ahvaz Water Treatment Plants

(2021) · Biointerface Research in Applied Chemistry · doi:10.33263/briac116.1403214043 · Cited by 10

Binary choice under asymmetric loss in a data‐rich environment: Theory and an application to algorithmic fairness

Babii, A., Chen, X., Ghysels, É., & Kumar, R. (2026) · Quantitative Economics · doi:10.3982/qe2385 · Cited by 10

Functional and Cognitive Grammars

Siewierska, A. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199585847.013.0022 · Cited by 10

Threshold logic implementations: the early days

Beiu, V. C. (2006) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2003.1562552 · Cited by 10

Analysis of Generic Perceptron-Like Large Margin Classifiers

Tsampouka, P., & Shawe-Taylor, J. S. (2005) · Lecture notes in computer science · doi:10.1007/11564096_77 · Cited by 10

A Biologically Inspired Approach to Long Term Autonomy and Survival in Sailing Robots

Sauzé, C., & Neal, M. (2008) · Cited by 10

A STUDY ON CHARACTER-PATTERNS BASED UPON THE THEORY OF PSYCHOLOGICAL POTENTIAL FIELD

Yokose, Z. (1970) · Japanese Psychological Research · doi:10.4992/psycholres1954.12.18 · Cited by 10

The Never-Ending Learning

Béroule, D. (1989) · Springer eBooks · doi:10.1007/978-3-642-83740-1_24 · Cited by 10

Dependency Parsing with Second-Order Feature Maps and Annotated Semantic Information

Ciaramita, M., & Attardi, G. (2010) · Text, speech and language technology · doi:10.1007/978-90-481-9352-3_6 · Cited by 10

Practical learning from one-sided feedback

Sculley, D. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281258 · Cited by 10

Blood pressure long term regulation: A neural network model of the set point development

Zanutto, B. S., Cernuschi-Frías, B., & Valentinuzzi, M. E. (2011) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-10-54 · Cited by 10

Approximate maximum margin algorithms with rules controlled by the number of mistakes

Tsampouka, P., & Shawe-Taylor, J. S. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273610 · Cited by 10

Forecasting of clustered time series with recurrent neural networks and a fuzzy clustering scheme

Seedig, H. G., Grothmann, R., & Runkler, T. A. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178775 · Cited by 10

Characterization of Volatile Compounds in New Cider Apple Genotypes Using Multivariate Analysis

Pello-Palma, J., Alonso, J. J. M., de la Fuente, E. D., González‐Álvarez, J., Díez, J., Gutiérrez-Álvarez, M. D., & Abrodo, P. A. (2016) · Food Analytical Methods · doi:10.1007/s12161-016-0521-7 · Cited by 10

Extending information relevant for personal dose monitoring obtained from glow curves of thermoluminescence dosimeters using artificial neural networks

Mentzel, F., Kroeninger, K. A., Röhrig, L., Speicher, L., Steil, M.-L., Theinert, R., & Walbersloh, J. (2020) · Radiation Measurements · doi:10.1016/j.radmeas.2020.106375 · Cited by 10

Deep Learning for Biomedical Data Analysis

(2021) · Springer eBooks · doi:10.1007/978-3-030-71676-9 · Cited by 10

Neural network based adaptive control

Camacho, E. F., & Arahal, M. R. (1994) · Annual Review in Automatic Programming · doi:10.1016/0066-4138(94)90037-x · Cited by 10

Neural networks versus nonparametric neighbor-based classifiers for semisupervised classification of Landsat Thematic Mapper imagery

Hardin, P. J. (2000) · Optical Engineering · doi:10.1117/1.602574 · Cited by 10

Neural networks and response surface polynomials for design of vehicle joints

Nikolaidis, E., Long, L., & Ling, Q. (1998) · AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (SDM) · doi:10.2514/6.1998-1777 · Cited by 10

Adaptive Critic Designs Based Neurocontrollers for Local and Wide Area Control of a Multimachine Power System with a Static Compensator

Mohagheghi, S. (2006) · Cited by 10

Elementary Thinking and the Classification of Behavior

Mark, H. J. (1962) · Science · doi:10.1126/science.135.3498.75 · Cited by 10

CONSTRUCTIVE TRAINING METHODS FOR FEEDFORWARD NEURAL NETWORKS WITH BINARY WEIGHTS

Mayoraz, E., & Aviolat, F. (1996) · International Journal of Neural Systems · doi:10.1142/s0129065796000129 · Cited by 10

Dynamic hierarchies: a control system paradigm for exposure therapy

Brady, A., & Raines, D. (2009) · The Cognitive Behaviour Therapist · doi:10.1017/s1754470x0800010x · Cited by 10

Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods

Gascuel, O., Bouchon‐Meunier, B., Caraux, G., Gallinari, P., Guénoche, A., Guermeur, Y., Yves Lechevallier, B., Marsala, C., Miclet, L., Nicolas, J., et al. (1998) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001498000336 · Cited by 10

Recall Systems: Effcient Learning and Use of Category Indices

Madani, O., Greiner, W., Kempe, D., & Salavatipour, M. R. (2007) · International Conference on Artificial Intelligence and Statistics · Cited by 10

Massive-scale Online Feature Selection for Sparse Ultra-high Dimensional Data.Preprint

Wu, Y., Hoi, S. C. H., & Mei, T. (2014) · arXiv (Cornell University) · Cited by 10

To The Knowledge Frontier and Beyond: A Hybrid System for Incremental Contextual- Learning and Prudence Analysis

Dazeley, R. (2006) · UTAS Research Repository · doi:10.25959/23210765 · Cited by 10

Active Learning with Expert AdvicePreprint

Zhao, P., Hoi, S. C. H., & Zhuang, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.6875 · Cited by 10

1965 Nobel Laureates in Medicine or Physiology

Stent, G. S. (1965) · Science · doi:10.1126/science.150.3695.462 · Cited by 10

Computer assisted interpretation of laboratory test data with ‘MDI-LabLink’

Regeniter, A., Siede, W. H., & Seiffert, U. B. (1996) · Clinica Chimica Acta · doi:10.1016/0009-8981(95)06271-8 · Cited by 10

THE PERCEPTRON: A PROBABILISTIC MODEL FOR INFORMATION STORAGE AND ORGANIZATION

Brain, I. T., & Rosenblatt, F. F. (2013)

The perception: a probabilistic model for information storage and organization in the brain

Rosenblatt, F. F. (1988) · The MIT Press eBooks · Cited by 78

(1958) F. Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain," Psychological Review 65: 386-408

Rosenblatt, F. F. (1988) · The MIT Press eBooks · doi:10.7551/mitpress/4943.003.0010 · Cited by 13

Perception, memory and providing information

Upton, D. (2013) · doi:10.4324/9781315846972-11

Brain mechanisms of perception and memory from neuron to behavior

Ono, T., & Toyama, I. S. O. B. M. O. P. A. M. F. N. T. B. 1. (1993)