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  Browse All Reviews > Computing Methodologies (I) > Pattern Recognition (I.5) > Design Methodology (I.5.2)  
 
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  1-10 of 161 Reviews about "Design Methodology (I.5.2)": Date Reviewed
  Image texture analysis: foundations, models and algorithms
Hung C., Song E., Lan Y., Springer International Publishing, New York, NY, 2019. 258 pp.  Type: Book (978-3-030137-72-4)

Texture analysis plays an important role in machine vision and pattern recognition. Along with the emergence of artificial intelligence (AI) comes an increase in applications requiring image texture analysis. Deep learning (DL)-based a...

Feb 18 2021
  "Why should I trust you?": Explaining the predictions of any classifier
Ribeiro M., Singh S., Guestrin C.  KDD 2016 (Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, Aug 13-17, 2016) 1135-1144, 2016.  Type: Proceedings

When Bohr introduced his theory of quantum jumps as a model of the inside of an atom, he said that quantum jumps exist but no one can visualize them. Thus, at the time, the scientific community was outraged because science is all about...

May 29 2020
  Feature selection and enhanced krill herd algorithm for text document clustering
Abualigah L., Springer International Publishing, New York, NY, 2019. 165 pp.  Type: Book (978-3-030106-73-7)

This monograph, which comes out of the author’s PhD thesis, studies text document clustering with the help of the krill herd (KH) algorithm....

May 10 2019
  Learning representation for multi-view data analysis: models and applications
Ding Z., Zhao H., Fu Y., Springer International Publishing, New York, NY, 2019. 268 pp.  Type: Book (978-3-030007-33-1)

Ding et al.’s Learning representation for multi-view data analysis not only provides cutting-edge research on multi-view data representation and analysis, but also provides several visual applications and practical cha...

May 7 2019
  Assessing and improving prediction and classification: theory and algorithms in C++
Masters T., Apress, New York, NY, 2018. 517 pp.  Type: Book (978-1-484233-35-1)

There is an increased interest in machine learning (ML) in both academy and industry. One consequence is a greater demand for textbooks and other educational materials that explain the different facets of ML to a heterogeneous readersh...

Nov 28 2018
  Prediction of HIV drug resistance by combining sequence and structural properties
Khalid Z., Sezerman O. IEEE/ACM Transactions on Computational Biology and Bioinformatics 15(3): 966-973, 2018.  Type: Article

Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) has been one of the deadliest diseases for the past three decades. Treatment is challenging even today....

Nov 7 2018
  Dedicated feature descriptor for outdoor augmented reality detection
Takacs A., Toledano-Ayala M., Pedraza-Ortega J., Rivas-Araiza E. Pattern Analysis & Applications 21(2): 351-362, 2018.  Type: Article

Much interest has been devoted to augmented reality (AR) in both academic and enterprise environments, and rightly so. AR enhances interaction with real-world objects by adding computer-generated information to these objects relevant t...

Jul 19 2018
  A filter attribute selection method based on local reliable information
Martín R., Aler R., Galván I. Applied Intelligence 48(1): 35-45, 2018.  Type: Article

In classification algorithms, the core problem is selecting the right attributes and assigning them the right weight for each item being processed, in order to achieve reliable results; all the more so if machine learning is involved. ...

Apr 25 2018
  Brain tumor classification from multi-modality MRI using wavelets and machine learning
Usman K., Rajpoot K. Pattern Analysis & Applications 20(3): 871-881, 2017.  Type: Article

Brain tumor detection poses serious challenges not only because the brain is a complex structure in itself, but also because tumors are serious and often fatal. Of course methods already exist to scan the brain, even in a state of cons...

Dec 15 2017
  A survey on ensemble learning for data stream classification
Gomes H., Barddal J., Enembreck F., Bifet A. ACM Computing Surveys 50(2): 1-36, 2017.  Type: Article

The automation of several processes, such as business transactions, smartphones, and various types of sensors, has severely increased the number of data stream generators. In data stream classification, data items are represented by a ...

Jun 16 2017
 
 
 
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