Yaser Abu-Mostafa | Academic Influence

Yaser Abu-Mostafa's AcademicInfluence.com Rankings

Yaser Abu-Mostafa

Computer Science

#1339

World Rank

#1384

Historical Rank

#677

USA Rank

Machine Learning

#104

World Rank

#105

Historical Rank

#37

USA Rank

Data Mining

#273

World Rank

#274

Historical Rank

#24

USA Rank

Artificial Intelligence

#1267

World Rank

#1291

Historical Rank

#204

USA Rank

computer-science Degrees

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Computer Science

Yaser Abu-Mostafa's Degrees

Bachelors Electrical Engineering and Computer Science Cairo University

Why Is Yaser Abu-Mostafa Influential?

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According to Wikipedia, Yaser Said Abu-Mostafa is Professor of Electrical Engineering and Computer Science at the California Institute of Technology, Chairman of Paraconic Technologies Ltd, and Chairman of Machine Learning Consultants LLC. He is known for his research and educational activities in the area of machine learning.

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Yaser Abu-Mostafa's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Recognitive Aspects of Moment Invariants (1984) (415)
Introduction to financial forecasting (1996) (320)
Learning from hints in neural networks (1990) (272)
Information capacity of the Hopfield model (1985) (268)
Image Normalization by Complex Moments (1985) (260)
Optical Neural Computers (1987) (234)
Learning From Data (2012) (194)
Pruning training sets for learning of object categories (2005) (157)
The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning (1989) (154)
On the K-Winners-Take-All Network (1988) (133)
Hints and the VC Dimension (1993) (119)
Hints (2018) (116)
The maximum drawdown of the Brownian motion (2003) (111)
On the maximum drawdown of a Brownian motion (2004) (93)
A Method for Learning From Hints (1992) (86)
Neural networks for computing (1987) (78)
No Free Lunch for Early Stopping (1999) (78)
Monotonicity Hints (1996) (73)
Learning and Measuring Specialization in Collaborative Swarm Systems (2004) (72)
Machines that Learn from Hints (1995) (71)
Introduction to the special issue on neural networks in financial engineering (2001) (46)
Emergent Specialization in Swarm Systems (2002) (43)
An analog feedback associative memory (1993) (42)
Financial markets: very noisy information processing (1998) (41)
Learning from Hints (1994) (37)
The Capacity of Multilevel Threshold Functions (1988) (36)
Complexity in Information Theory (1988) (35)
Information theory, complexity and neural networks (1989) (33)
Financial model calibration using consistency hints (2001) (30)
Robust image recognition by fusion of contextual information (2002) (28)
The complexity of information extraction (1986) (27)
Improving Generalization by Data Categorization (2005) (26)
Decision Technologies for Financial Engineering (1998) (26)
Connectivity Versus Entropy (1987) (24)
Data complexity in machine learning and novel classification algorithms (2006) (22)
From ordinal ranking to binary classification (2008) (22)
Financial Applications of Learning from Hints (1994) (22)
Data Complexity in Machine Learning (2006) (21)
Complexity of random problems (1988) (21)
Analog Neural Networks as Decoders (1990) (19)
Mismatched Training and Test Distributions Can Outperform Matched Ones (2015) (18)
Maximal codeword lengths in Huffman codes (1992) (17)
Theory of Neural Networks (1991) (17)
Machines that Think for Themselves (2012) (16)
Generalization error estimates and training data valuation (2002) (15)
Computational finance 1999 (2000) (15)
Decision Technologies for Financial Engineering: Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (NNCM '96) (1998) (13)
CGBoost: Conjugate Gradient in Function Space (2003) (12)
The Multilevel Classification Problem and a Monotonicity Hint (2002) (12)
Random problems (1988) (10)
Incorporating Contextual Information in White Blood Cell Identification (1997) (9)
Image Recognition in Context: Application to Microscopic Urinalysis (1999) (9)
DIVERSITY AND SPECIALIZATION IN COLLABORATIVE SWARM SYSTEMS (2003) (9)
An algorithm for learning from hints (1993) (6)
Lower bound for connectivity in local-learning neural networks (1988) (6)
A Method for the Associative Storage of Analog Vectors (1989) (6)
Machine Learning for Recession Prediction and Dynamic Asset Allocation (2019) (5)
Neutral networks for computing (2008) (4)
Validation of volatility models (1998) (4)
The Bin Model (2004) (3)
The United States COVID-19 Forecast Hub dataset (2022) (3)
Machines that think for themselves: new techniques for teaching computers how to learn are beating the experts. (2012) (3)
Nowcasting Recessions using the SVM Machine Learning Algorithm (2019) (2)
Minimizing memory loss in learning a new environment (2001) (1)
Pointwise Universality of the Normal Form (1987) (1)
Introduction to the Theory of Neural Computation {Book Reviews] (1996) (1)
On the Time-Bandwidth Proof in VLSI Complexity (1987) (1)
A Differentiation Test for Absolute Convergence (1984) (1)
42-110 August 15 , 1992 / ( Maximal Codeword Lengths in Huffman Codes (0)
Monotonicity: theory and implementation (1997) (0)
A Zernike Moment based Modified CBIR System with Canny Edge Detector (2020) (0)
Four Results in Matching Data Distributions (2014) (0)
Two Theorems on Time Bounded Kolmogrov-Chaitin Complexity (1985) (0)
Review of 'Introduction to the Theory of Neural Computation' (Hertz, J., et al.; 1991) (1996) (0)
County-Specific, Real-Time Projection of the Effect of Business Closures on the COVID-19 Pandemic (2021) (0)
The Multilevel Classi ation Problem and aMonotoni ity (2007) (0)
How Hints Affect Learning (1993) (0)
Essential Average Mutual Information (1987) (0)
Estimating Model Limitation in FinancialMarketsMalik (1998) (0)
Theoretical investigation of optical computing based on neural network models (1987) (0)
Neural networks (1990) (0)
Efficiency of computation in neural networks (1985) (0)
Machine-learning Paradigm Machines That Learn from Hints Machine Learning Improves Significantly by Taking Advantage of Information Available from Intelligent Hints (0)

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