363,972 Hits in 5.2 sec

An Online Universal Classifier for Binary, Multi-class and Multi-label Classification [article]

Meng Joo Er, Rajasekar Venkatesan, Ning Wang
2016 arXiv   pre-print
Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification  ...  Traditional binary and multi-class classifications are sub-categories of single-label classification.  ...  From the table, it is evident that the proposed classifier is capable of performing classification of all types with high speed, thus facilitating its application for real-time streaming data. C.  ... 
arXiv:1609.00843v1 fatcat:tu5tiglymfbfhfivpngs57kfay

Detecting Similar Versions of Software by Learning with Logistic Regression on Binary Opcode Information [chapter]

Hyun-il Lim
2020 Frontiers in Artificial Intelligence and Applications  
The proposed logistic regression model is expected to be applied in applications for comparing and detecting similar versions of software.  ...  Because the binary opcode information has detailed information for executing software on an individual machine, the learning from the binary opcode information can provide effective information in detecting  ...  For the practical application of the proposed model for detecting similar versions of software, it needs to generalize the execution environment to adapt data with various types of similar versions.  ... 
doi:10.3233/faia200776 fatcat:5q77zmcc3rgypeist3ryadqhq4

Machine Learning-based Analysis of Program Binaries: A Comprehensive Study

Hongfa Xue, Shaowen Sun, Guru Venkataramani, Tian Lan
2019 IEEE Access  
Finally, we present our thoughts for future directions on this topic. INDEX TERMS Machine learning, program binary analysis, taxonomy.  ...  Traditionally adopted techniques for binary code analysis are facing multiple challenges, such as the need for cross-platform analysis, high scalability and speed, and improved fidelity, to name a few.  ...  OTHER TYPES OF APPLICATIONS Besides the applications we have mentioned above, there are several other works using machine learning based BCA frameworks for other purposes.  ... 
doi:10.1109/access.2019.2917668 fatcat:fwjpykkdpjev7pzkhaoily4zci

A Performance Evaluation of Local Features for Image Based 3D Reconstruction [article]

Bin Fan and Qingqun Kong and Xinchao Wang and Zhiheng Wang and Shiming Xiang and Chunhong Pan and Pascal Fua
2017 arXiv   pre-print
However, for the case of large scale image set with many distracting images, float type features show a clear advantage over binary ones.  ...  To obtain a comprehensive evaluation, we choose to include both float type features and binary ones. Meanwhile, two kinds of datasets have been used in this evaluation.  ...  For float type descriptors, it includes SIFT and LIOP as representative handcrafted ones and covers the learning based ones that use the traditional learning technique (VGGDesc) and the recently popular  ... 
arXiv:1712.05271v1 fatcat:t3n2noer2vgtthd2vmmxxepddy

Malware Detection at the Microarchitecture Level using Machine Learning Techniques [article]

Abigail Kwan
2020 arXiv   pre-print
This research comprehensively analyzes different hardware-based malware detectors by comparing different machine learning algorithms' accuracy with binary and multi-class classification models.  ...  Neural Network and Logistic), light-weight J48 and JRip algorithms perform better in detecting the malicious patterns even with the introduction of multiple types of malware.  ...  For malware detection, it means that the machine learning algorithm must identify if a software is a specific type of malware.  ... 
arXiv:2005.12019v1 fatcat:oik6razrajdubpi3cotctjr6nm

Design and Analysis of Binary Scalar Quantizer of Laplacian Source with Applications

Zoran Peric, Bojan Denic, Milan Savic, Vladimir Despotovic
2020 Information  
A compression method based on non-uniform binary scalar quantization, designed for the memoryless Laplacian source with zero-mean and unit variance, is analyzed in this paper.  ...  The experimental results follow well the theoretical models, confirming their applicability in real-world applications.  ...  However, it is also of interest to Note that for this specific case (i.e., using the optimal values of x clip ), both binary quantizer type 1 and binary quantizer type 2 guarantee the same performance.  ... 
doi:10.3390/info11110501 fatcat:evmv3a243fd2rdp7f6miuhkpmm

Comparison of Multi-Label Classification Methods for Prediagnosis of Cervical Cancer

Zeynep Ceylan
2017 International Journal of Intelligent Systems and Applications in Engineering  
Recently, different types of advanced methods were developed for risk prediction analysis based on machine learning techniques.  ...  Four common learning algorithms such as Naïve Bayes, J48 Decision Tree, Sequential Minimal Optimization, and Random Forest were compared in terms of their accuracy, hamming loss, exact match (subset accuracy  ...  Binary Relevance (BR) is a well-known and the most popular transformation method that learns q binary classifiers; one for each possible labels in L.  ... 
doi:10.18201/ijisae.2017533896 fatcat:3642gse3yzanbhjsgsfeh4kkni

User Authentication by Keystroke Dynamics Using Machine Learning Algorithms

Najla Alavi, Kasim K
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
Three schemes are proposed for recognizing an individual while typing on keyboard. Lib SVM and binary SVM are proposed and their performance are shown.  ...  Lib SVM is showing a better performance when comparing with binary SVM. As the number of samples are increased it shows an increase in the accuracy. Pair wise user coupling technique is proposed.  ...  Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  ... 
doi:10.32628/cseit1953137 fatcat:ufg4sdt6svenlip7yf5xpmhesm

Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization

Muhammet Fatih Aslan, Akif Durdu, Kadir Sabanci
2019 Neural computing & applications (Print)  
Moreover, four different machine learning (ML) methods such as k-nearest neighbors, decision tree, support vector machine and naive Bayes are used for classification of BoVW features.  ...  Human activity recognition (HAR) has quite a wide range of applications. Due to its widespread use, new studies have been developed to improve the HAR performance.  ...  Acknowledgements The authors are thankful to RAC-LAB (www. for providing the trial version of their commercial software for this study.  ... 
doi:10.1007/s00521-019-04365-9 fatcat:dbgklkcuj5a6rgv65dtkj6szci

Two Stage Deep Learning Based Stacked Ensemble Model for Web Application Security

2022 KSII Transactions on Internet and Information Systems  
In this study, we aim to develop a robust two-stage deep learning based stacked ensemble web application firewall.  ...  The proposed two-stage model achieved multi-class detection rates of 97.43% and 94.77% for GAZI-HTTP and ECML-PKDD, respectively.  ...  It is seen that Nguyen and et al. [10] have reached the highest classification success so far with a detection rate of 98.80% for binary classification on ECML-PKDD.  ... 
doi:10.3837/tiis.2022.02.014 fatcat:wemggxag3reedgr6mpn34rnnau

Collaborative Hashing

Xianglong Liu, Junfeng He, Cheng Deng, Bo Lang
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
By simultaneously preserving both the entity similarities in each view and the interrelationship between views, our collaborative hashing effectively learns the compact binary codes and the explicit hash  ...  Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities.  ...  Acknowledgement This work is supported in part by NSFC 61370125 and 61101250, NCET-12-0917, SKLSDE-2013ZX-05 and SKLSDE-2014ZX-07.  ... 
doi:10.1109/cvpr.2014.275 dblp:conf/cvpr/LiuHDL14 fatcat:6kvtxvmf2begbaecjovue65g7m

iCallee: Recovering Call Graphs for Binaries [article]

Wenyu Zhu, Zhiyao Feng, Zihan Zhang, Zhijian Ou, Min Yang, Chao Zhang
2021 arXiv   pre-print
To show its usefulness, we apply iCallee to two specific applications - binary code similarity detection and binary program hardening, and found that it could greatly improve state-of-the-art solutions  ...  Existing indirect callee recognition solutions for binaries all have high false positives and negatives, making call graphs inaccurate.  ...  However, it is very hard to learn precise semantic and behavior information from these static features. And thus static program embedding in general is not fit for machine learning solutions.  ... 
arXiv:2111.01415v2 fatcat:3dt3wngiyrbohk3gahv45pykzu

Logistic Regression for Health Profiling

2019 International Journal of Engineering and Advanced Technology  
For any binary classification problem it is very easy to use as a basic approach. Deep learning is also its fundamental concept.  ...  For two class classification the Logistic Regression is one of the most simple and common machine Learning algorithms.  ...  The logit model is a kind of statistical analysis and it is often used reaches out to applications in machine learning and for prescient investigation and displaying. in this approach the dependent variable  ... 
doi:10.35940/ijeat.f1294.0886s219 fatcat:xdokqwlvybcyjh2kbg22w2tiee

binary junipr: An Interpretable Probabilistic Model for Discrimination

Anders Andreassen, Ilya Feige, Christopher Frye, Matthew D. Schwartz
2019 Physical Review Letters  
Separate JUNIPR models can be learned for different event or jet types, then compared and explored for physical insight. The relative probabilities can also be used for discrimination.  ...  We refer to this refined approach as Binary JUNIPR. Binary JUNIPR achieves state-of-the-art performance for quark/gluon discrimination and top-tagging.  ...  Note that BINARY JUNIPR still learns the probabilities for type-a and type-b jets and still trains the same neural-network functions; however, it uses a more effective objective function for discrimination  ... 
doi:10.1103/physrevlett.123.182001 fatcat:25mpu7nlcrhghjtnyxdcytr6fi

A Simple Classification of Binary Document into Vector Image or Scalar Image using Feed Forward Neural Networks with Back Propagation Training

G. Sudha
2010 International Journal of Computer Applications  
Currently, this synergistically developed back-propagation architecture is the most popular, effective, and easy to learn model for complex, multi-layered networks and suitable for different types of applications  ...  Such type of paper based information is converted routinely done for records management, automated document delivery, document archiving, distribution and other related applications.  ... 
doi:10.5120/1365-1840 fatcat:cc4kilw2pjhylcxu62bi5x6ljq
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