Hierarchical Learning for Fine Grained Internet Traffic Classification
... detailed discussion of related work. Both DPI and behavioral classifiers are supervised techniques. However, in case of DPI, the training is often cumbersome and complex, since it involves in most cases the manual identification of the tokens and regular expressions that define a class. In case of b ...
... detailed discussion of related work. Both DPI and behavioral classifiers are supervised techniques. However, in case of DPI, the training is often cumbersome and complex, since it involves in most cases the manual identification of the tokens and regular expressions that define a class. In case of b ...
A fuzzy decision tree approach to start a genetic
... The fuzzy trees with up to 5 leaves are the ones with a unique decision node. So the conversion of them into TSK rules produced equivalent classifiers because each leaf was identical to a rule. When the amount of leaves was higher, this equivalence does not occur and Table 3 shows the conversions pe ...
... The fuzzy trees with up to 5 leaves are the ones with a unique decision node. So the conversion of them into TSK rules produced equivalent classifiers because each leaf was identical to a rule. When the amount of leaves was higher, this equivalence does not occur and Table 3 shows the conversions pe ...
Intelligence Based Intrusion Detection System (IBIDS) Senior Project
... network has some private data on the internet, whether it be a simple hotmail account password, and on his own computer. This information needs to be secured and kept safe from all kinds of intruders so that the safety and privacy of the individual is maintained. Intrusion Detection Systems are devi ...
... network has some private data on the internet, whether it be a simple hotmail account password, and on his own computer. This information needs to be secured and kept safe from all kinds of intruders so that the safety and privacy of the individual is maintained. Intrusion Detection Systems are devi ...
CS 245A Intelligent Information Systems
... group similar objects into a more general object. This generalization hierarchy defines the inheritance structure. Classification: Classification provides a means whereby specific object instances can be considered as a higher-level object-type (an object-type is a collection of similar objects). Th ...
... group similar objects into a more general object. This generalization hierarchy defines the inheritance structure. Classification: Classification provides a means whereby specific object instances can be considered as a higher-level object-type (an object-type is a collection of similar objects). Th ...
opinion
... • Id: Abc123 on 5-1-2008 “I bought an iPhone a few days ago. It is such a nice phone. The touch screen is really cool. The voice quality is clear too. It is much better than my old Blackberry, which was a terrible phone and so difficult to type with its tiny keys. However, my mother was mad with me ...
... • Id: Abc123 on 5-1-2008 “I bought an iPhone a few days ago. It is such a nice phone. The touch screen is really cool. The voice quality is clear too. It is much better than my old Blackberry, which was a terrible phone and so difficult to type with its tiny keys. However, my mother was mad with me ...
Data Analytics for Customer Facing Applications
... subject areas of the corporation that have been defined in the data model. E.g. for an insurance company: customer, product, transaction or activity, policy, claim, account, and etc. ...
... subject areas of the corporation that have been defined in the data model. E.g. for an insurance company: customer, product, transaction or activity, policy, claim, account, and etc. ...
A decentralized approach for mining event correlations in distributed
... mining can hardly be done with the power of a single computer in acceptable time. In contrast, decentralized data mining is particularly suitable for applications that typically deal with very large amounts of data (e.g., transaction data, scientific simulation, and telecom data) that cannot be anal ...
... mining can hardly be done with the power of a single computer in acceptable time. In contrast, decentralized data mining is particularly suitable for applications that typically deal with very large amounts of data (e.g., transaction data, scientific simulation, and telecom data) that cannot be anal ...
Locally Linear Reconstruction: Classification performance
... A distance & local topology-based hybrid score is employed for keystroke dynamics-based user authentication. KDA: to authenticate users based on their keyboard typing behaviors. KDA should be formulated as a novelty detection problem. An authenticator should work well for incremental environments. A ...
... A distance & local topology-based hybrid score is employed for keystroke dynamics-based user authentication. KDA: to authenticate users based on their keyboard typing behaviors. KDA should be formulated as a novelty detection problem. An authenticator should work well for incremental environments. A ...
Opinion Mining
... frequently, when #micro-blogs is more than 10,000, AP is much too slow! To scale up the AP, we have got 3 perspectives: • Sampling or Pruning to reduce #micro-blogs • Give a sketch of the matrix and store those values only above a predefined threshold • Use a new similarity measurement and make th ...
... frequently, when #micro-blogs is more than 10,000, AP is much too slow! To scale up the AP, we have got 3 perspectives: • Sampling or Pruning to reduce #micro-blogs • Give a sketch of the matrix and store those values only above a predefined threshold • Use a new similarity measurement and make th ...
Research of an Improved Apriori Algorithm in Data Mining
... However, Ck might be very large, and then the amount of calculation will be huge. In order to decrease Ck, there are following method using the prosperities of Apriori: any infrequent item sets with k-1 items are not the subset of frequent item sets with k items. Consequently, if the (k-1) subset of ...
... However, Ck might be very large, and then the amount of calculation will be huge. In order to decrease Ck, there are following method using the prosperities of Apriori: any infrequent item sets with k-1 items are not the subset of frequent item sets with k items. Consequently, if the (k-1) subset of ...
A PROPOSED DATA MINING DRIVEN METHDOLOGY FOR
... posture, gesture) while about 35% is considered verbal (e.g., speech, discussion) [1]. The verbal human communication component conveys a large volume of information, but may miss latent aspects such as body posture, facial gestures, etc. that may provide researchers with added dimensions of knowled ...
... posture, gesture) while about 35% is considered verbal (e.g., speech, discussion) [1]. The verbal human communication component conveys a large volume of information, but may miss latent aspects such as body posture, facial gestures, etc. that may provide researchers with added dimensions of knowled ...
Big Data Analytics
... Big Data Analytics Lucas Rego Drumond Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany ...
... Big Data Analytics Lucas Rego Drumond Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany ...
Data Mining
... data available that need to be turned into useful information. • It is nothing but extraction of data from large databases for some specialized work. • This information further can be used for various applications such as consumer research marketing, product analysis, demand and supply analysis, e-c ...
... data available that need to be turned into useful information. • It is nothing but extraction of data from large databases for some specialized work. • This information further can be used for various applications such as consumer research marketing, product analysis, demand and supply analysis, e-c ...
A Local Discretization of Continuous Data for Lattices: Technical Aspects
... M I is split into two intervals as explain before. The context T is then updated with these new intervals; and its M I are computed. The process is iterated until all M ∈ M I verify the stopping criterion S. The context T is initialized with, for each continuous attribute, an interval -i.e. a binary ...
... M I is split into two intervals as explain before. The context T is then updated with these new intervals; and its M I are computed. The process is iterated until all M ∈ M I verify the stopping criterion S. The context T is initialized with, for each continuous attribute, an interval -i.e. a binary ...
CAB Algorithms Presentation
... Preview of the new Oracle Data Miner 11g R2 “work flow” New GUI Oracle Data Mining 11gR2 presentation at Oracle Open World 2009 Oracle Data Mining Blog Funny YouTube video that features Oracle Data Mining Oracle Data Mining on the Amazon Cloud Oracle Data Mining 11gR2 data sheet Oracle Data Mining 1 ...
... Preview of the new Oracle Data Miner 11g R2 “work flow” New GUI Oracle Data Mining 11gR2 presentation at Oracle Open World 2009 Oracle Data Mining Blog Funny YouTube video that features Oracle Data Mining Oracle Data Mining on the Amazon Cloud Oracle Data Mining 11gR2 data sheet Oracle Data Mining 1 ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.