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[PDF] Top 20 Topological data analysis: applications in machine learning

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Topological data analysis: applications in machine learning

Topological data analysis: applications in machine learning

... development in data analysis giving birth to the field of Topological Data ...of data that can be represented as points in metric space. In this work, we apply ... See full document

124

Identification of video applications over protected channels with machine learning

Identification of video applications over protected channels with machine learning

... described in Section 2.4. Thus, with the need for protection of data and user privacy becoming exponentially important, making approaches that depend on content inspection raises ethical ...massive ... See full document

105

Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon

Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon

... and machine learning techniques, tools which are capable of recognizing patterns in a large soil ...chemical data of 1,068 profiles of the RadamBrasil Project were ...component analysis ... See full document

20

Modeling system based on machine learning approaches for predictive maintenance applications

Modeling system based on machine learning approaches for predictive maintenance applications

... Fault recognition accuracy predictions are essential to reduce uncertainties in the near future and thus anticipate the state of failure to make the best decisions. However, before such methods can be implemented, ... See full document

15

Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques.

Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques.

... Sentiment analysis is basically concerned with analysis of emotions and opinions from ...sentiment analysis as opinion mining. Sentiment analysis finds and justifies the sentiment of the ... See full document

7

Big Data, machine learning and challenges of high dimensionality in financial administration

Big Data, machine learning and challenges of high dimensionality in financial administration

... the in-sample error and the model’s ...present in the training sample, without exploiting it with an excessively com- plex decision ...frequency data can provide ulterior information over low ... See full document

241

Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

... difference in the performance of different classification ...all machine learning techniques for software bug ...different data mining techniques for software bug prediction but did not ... See full document

9

A Cloud-Based Framework for Machine Learning Workloads and Applications

A Cloud-Based Framework for Machine Learning Workloads and Applications

... same data and means as were used by the original ...community, in fact, has gained awareness about the importance of following some basic reproducibility principles on their research [56], increasing thus ... See full document

12

Learning from HTTP/2 encrypted traffic: a machine learning-based analysis tool

Learning from HTTP/2 encrypted traffic: a machine learning-based analysis tool

... stream in text format with the contents of every packet sent or received in that stream, and then reassemble the packets by using txt2pcap to con- vert the byte dump into a PCAP ...partial analysis ... See full document

66

Machine learning nonlocal correlations

Machine learning nonlocal correlations

... measurement data, one can prove the quantumness of some observed ...many applications in near term quantum tech- nologies such as quantum cryptography [4–6] ...a machine learning ... See full document

6

Machine learning over encrypted data = Aprendizagem de máquina sobre dados cifrados

Machine learning over encrypted data = Aprendizagem de máquina sobre dados cifrados

... work, in the paper [21], LHE was used to evaluate the binary clas- sifiers Linear Means ans Fisher’s Linear Discriminant over encrypted ...present in the original ...incurring in high communication ... See full document

65

Exploration and application of machine learning algorithms to functional connectivity data

Exploration and application of machine learning algorithms to functional connectivity data

... extended data through statistical parametric maps (Li et al., 2009). In few words, SPM uses GLM to estimate the parameters that could explain the data and uses GRF to resolve the multiple comparison ... See full document

94

Machine learning in supply chain management

Machine learning in supply chain management

... of data every hour and is based on the database management system SAP ...ML-algorithms. In addition to the transaction data from the physical and online stores the calculations integrate about 200 ... See full document

38

Sentiment analysis in geo social streams by using machine learning technique

Sentiment analysis in geo social streams by using machine learning technique

... used in this visualization. Realtime fetches data every 3 seconds from an API and provides data to clusters and ...the data integration is efficient. So far we have not felt any lag in ... See full document

67

Machine learning in analytical chemistry: applying innovative data analysis methods using chromatographic techniques

Machine learning in analytical chemistry: applying innovative data analysis methods using chromatographic techniques

... (chemical analysis). Its application in ML research, is responsible for interesting achievements such as top performances in ML ...competitions. In computer vision ...more data so it ... See full document

81

Malware classification on time series data through machine learning

Malware classification on time series data through machine learning

... classifiers. In fact, an assignment made by a single classifier might change through time, as a consequence of methods refinements or new ...this analysis by collecting historical data on files that ... See full document

57

MACHINE LEARNING TECHNIQUES USED IN BIG DATA

MACHINE LEARNING TECHNIQUES USED IN BIG DATA

... Big Data technologies year, while the 2013 could represent the year of data ...are data collecting and data management, but a challenge represents the extraction of helpful information from ... See full document

6

Improving eQTL Analysis Using a Machine Learning Approach for Data Integration

Improving eQTL Analysis Using a Machine Learning Approach for Data Integration

... the data among the ...majority. In this work, we consider a binary classification problem, that is, a problem having two classes where one contains most of the examples (majority class) while the other one ... See full document

42

Machine Learning for Supermarket Data Analysis

Machine Learning for Supermarket Data Analysis

... items in sequences which plays important roles on data analysis and knowledge ...patterns. In addition, the clustering analysis is used to automatically generate the suitable time ... See full document

72

Cross-platform normalization of microarray and RNA-seq data for machine learning applications

Cross-platform normalization of microarray and RNA-seq data for machine learning applications

... these data, revealing that this result was the less likely to result from ...these data, given that even the untransformed dataset had close to the same accuracy but did not have as strong a Kappa ... See full document

19

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