Man, machine, and strategy
... plan didn’t quite deliver the results you wanted. There is much hype over big data and new analytical technologies, some of it justified, much of it not. We are interested in whether, and how, technology can practically improve your business strategy. We believe man’s ability to think and formulate ...
... plan didn’t quite deliver the results you wanted. There is much hype over big data and new analytical technologies, some of it justified, much of it not. We are interested in whether, and how, technology can practically improve your business strategy. We believe man’s ability to think and formulate ...
CETIS Analytics Series vol 1, No 9. A Brief History of Analytics
... statistics in the eighteenth century but since then different applications of statistics and IT have led to different communities of practice that now seem to be merging together. We see that Web Analytics pioneers are now exploiting data from the “social web” by using Social Network Analysis and th ...
... statistics in the eighteenth century but since then different applications of statistics and IT have led to different communities of practice that now seem to be merging together. We see that Web Analytics pioneers are now exploiting data from the “social web” by using Social Network Analysis and th ...
Special Issue on Semantic Web Meets
... President – Marios M. Polycarpou, University of Cyprus, CYPRUS ...
... President – Marios M. Polycarpou, University of Cyprus, CYPRUS ...
FS-FOIL: An Inductive Learning Method for Extracting Interpretable
... machine learning problem is to find a function f : X1 × · · · × Xn −→ Xn+1 × · · · × Xn+m such that the inherent connection between the input attributes and the goal attribute hidden in the data set X is modeled as well as possible. Therefore, such machine learning problems can be regarded as some k ...
... machine learning problem is to find a function f : X1 × · · · × Xn −→ Xn+1 × · · · × Xn+m such that the inherent connection between the input attributes and the goal attribute hidden in the data set X is modeled as well as possible. Therefore, such machine learning problems can be regarded as some k ...
Revisiting Evolutionary Fuzzy Systems
... We want also to point out the significance of EFSs by studying all types of Data Mining problems in which they have shown a good behavior. In this way, we will stress the adapting capabilities of these systems by providing an exhaustive list of areas such as regression, classification, association rul ...
... We want also to point out the significance of EFSs by studying all types of Data Mining problems in which they have shown a good behavior. In this way, we will stress the adapting capabilities of these systems by providing an exhaustive list of areas such as regression, classification, association rul ...
Machine Learning I - Mit - Massachusetts Institute of Technology
... My favorite quote on the subject is the following, exerpted from Bertrand Russell's "On Induction": (see http://www.ditext.com/russell/rus6.html for the whole thing) If asked why we believe the sun will rise tomorrow, we shall naturally answer, 'Because it has always risen every day.' We have a firm ...
... My favorite quote on the subject is the following, exerpted from Bertrand Russell's "On Induction": (see http://www.ditext.com/russell/rus6.html for the whole thing) If asked why we believe the sun will rise tomorrow, we shall naturally answer, 'Because it has always risen every day.' We have a firm ...
Handling the Class Imbalance Problem in Binary Classification
... Natural processes often generate some observations more frequently than others. These processes result in an unbalanced distributions which cause the classifiers to bias toward the majority class especially because most classifiers assume a normal distribution. The quantity and the diversity of imba ...
... Natural processes often generate some observations more frequently than others. These processes result in an unbalanced distributions which cause the classifiers to bias toward the majority class especially because most classifiers assume a normal distribution. The quantity and the diversity of imba ...
Ensemble Learning Techniques for Structured
... encountered by service industries and addresses how these textual reviews can be used for service improvements. A service improvement framework is introduced that integrates traditional text mining techniques and second order feature derivation with ensemble learning techniques. The concept of GLOW ...
... encountered by service industries and addresses how these textual reviews can be used for service improvements. A service improvement framework is introduced that integrates traditional text mining techniques and second order feature derivation with ensemble learning techniques. The concept of GLOW ...
Artificial Intelligence: The Ultimate Technological Disruption Ascends
... most lenient measures, the Turing Test has already been passed by certain subcategories of AI, since certain chat bots can communicate at parity with humans. If a stricter standard is required, there is a public bet between Ray Kurzweil and Mitch Kapor regarding whether AI will be able to pass the T ...
... most lenient measures, the Turing Test has already been passed by certain subcategories of AI, since certain chat bots can communicate at parity with humans. If a stricter standard is required, there is a public bet between Ray Kurzweil and Mitch Kapor regarding whether AI will be able to pass the T ...
Optimal Ensemble Construction via Meta-Evolutionary
... Recently many researchers have combined the predictions of multiple classifiers to produce a better classifier, an ensemble, and often reported improved performance [1–3]. Bagging [4] and Boosting [5,6] are the most popular methods for creating accurate ensembles. Bagging is a bootstrap ensemble met ...
... Recently many researchers have combined the predictions of multiple classifiers to produce a better classifier, an ensemble, and often reported improved performance [1–3]. Bagging [4] and Boosting [5,6] are the most popular methods for creating accurate ensembles. Bagging is a bootstrap ensemble met ...
. - Villanova Computer Science
... which takes advantage of irrelevant variations in instances – performance on test data will be much lower – may mean that your training sample isn’t representative – in SVMs, may mean that C is too high ...
... which takes advantage of irrelevant variations in instances – performance on test data will be much lower – may mean that your training sample isn’t representative – in SVMs, may mean that C is too high ...
Consensus group stable feature selection
... of selected features for biomarker identification. In this paper, we demonstrate that stability of feature selection has a strong dependency on sample size. Moreover, we show that exploiting intrinsic feature groups in the underlying data distribution is effective at alleviating the effect of small ...
... of selected features for biomarker identification. In this paper, we demonstrate that stability of feature selection has a strong dependency on sample size. Moreover, we show that exploiting intrinsic feature groups in the underlying data distribution is effective at alleviating the effect of small ...
differential evolution based classification with pool of
... I consider it a blessing to have had the opportunity to carry out research work. It would not have been possible without the support of many wonderful people. I am not able to mention everyone here, but I acknowledge and deeply appreciate all your invaluable assistance and support. I would like to t ...
... I consider it a blessing to have had the opportunity to carry out research work. It would not have been possible without the support of many wonderful people. I am not able to mention everyone here, but I acknowledge and deeply appreciate all your invaluable assistance and support. I would like to t ...
Artificial Intelligence and Robotics and Their Impact on the Workplace
... Strong artificial intelligence: The processes in the computer are intellectual, self-learning processes. Computers can ‘understand’ by means of the right software/programming and are able to optimise their own behaviour on the basis of their former behaviour and their experience.4 This includes auto ...
... Strong artificial intelligence: The processes in the computer are intellectual, self-learning processes. Computers can ‘understand’ by means of the right software/programming and are able to optimise their own behaviour on the basis of their former behaviour and their experience.4 This includes auto ...
Leveraging the upcoming disruptions from AI and IoT
... and engage with customers. However, tapping into the IoT is only part of the story. For companies to realise the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable ‘smart machines’ to simulate intelligent behaviou ...
... and engage with customers. However, tapping into the IoT is only part of the story. For companies to realise the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable ‘smart machines’ to simulate intelligent behaviou ...
Leveraging the upcoming disruptions from AI and IoT
... and engage with customers. However, tapping into the IoT is only part of the story. For companies to realise the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable ‘smart machines’ to simulate intelligent behaviou ...
... and engage with customers. However, tapping into the IoT is only part of the story. For companies to realise the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable ‘smart machines’ to simulate intelligent behaviou ...
sociallocker - Projectsgoal
... In this paper, we propose a simple yet efficient model, called dual sentiment analysis (DSA), to address the polarity shift problem in sentiment classification. By using the property that sentiment classification has two opposite class labels (i.e., positive and negative), we first propose a data ex ...
... In this paper, we propose a simple yet efficient model, called dual sentiment analysis (DSA), to address the polarity shift problem in sentiment classification. By using the property that sentiment classification has two opposite class labels (i.e., positive and negative), we first propose a data ex ...
Subspace Clustering, Ensemble Clustering, Alternative Clustering
... To improve over several self-contained classifiers by building an ensemble of those classifiers requires the base algorithms being accurate (i.e., at least better than random) and diverse (i.e., making different errors on new instances). It is easy to understand why these two conditions are necessar ...
... To improve over several self-contained classifiers by building an ensemble of those classifiers requires the base algorithms being accurate (i.e., at least better than random) and diverse (i.e., making different errors on new instances). It is easy to understand why these two conditions are necessar ...
Big Data Analytics Using Neural networks
... and Mr. Sri Ram, for their important suggestions. At last, I would like thank my family for their encouraging support through the course of the project. ...
... and Mr. Sri Ram, for their important suggestions. At last, I would like thank my family for their encouraging support through the course of the project. ...
Large-scale attribute selection using wrappers
... of potential subset extensions decreases with each step, as in SFS, while the currently selected subset grows. 2) Fixed Width: This method keeps the number of extensions in each forward selection step constant to a fixed width k (see Figure 1(b)). Again, an initial ranking is calculated based on the ...
... of potential subset extensions decreases with each step, as in SFS, while the currently selected subset grows. 2) Fixed Width: This method keeps the number of extensions in each forward selection step constant to a fixed width k (see Figure 1(b)). Again, an initial ranking is calculated based on the ...
Large-scale attribute selection using wrappers
... of potential subset extensions decreases with each step, as in SFS, while the currently selected subset grows. 2) Fixed Width: This method keeps the number of extensions in each forward selection step constant to a fixed width k (see Figure 1(b)). Again, an initial ranking is calculated based on the ...
... of potential subset extensions decreases with each step, as in SFS, while the currently selected subset grows. 2) Fixed Width: This method keeps the number of extensions in each forward selection step constant to a fixed width k (see Figure 1(b)). Again, an initial ranking is calculated based on the ...
Slide 1
... required expertise for solving some aspects of the problem and provide knowledge that can enhance the operation of other DSS components ...
... required expertise for solving some aspects of the problem and provide knowledge that can enhance the operation of other DSS components ...
Profiles in Innovation: Artificial Intelligence
... Data. There has been massive growth in the amount of unstructured data being created by the increasingly ubiquitous connected devices, machines, and systems globally. Neural networks become more effective the more data that they have, meaning that as the amount of data increases the number of proble ...
... Data. There has been massive growth in the amount of unstructured data being created by the increasingly ubiquitous connected devices, machines, and systems globally. Neural networks become more effective the more data that they have, meaning that as the amount of data increases the number of proble ...
The SAS System and Meta Knowledge
... As explained by Y.DODGE and D.J.HAND in a paper entitled What Should Future Statistical Software Look Like? published in the November 1991 issue of the Statistical Software Newsletter "Meta-data is information about data and can obviously be used to help the user of statistical software. More exciti ...
... As explained by Y.DODGE and D.J.HAND in a paper entitled What Should Future Statistical Software Look Like? published in the November 1991 issue of the Statistical Software Newsletter "Meta-data is information about data and can obviously be used to help the user of statistical software. More exciti ...
PTE: Predictive Text Embedding through Large-scale
... and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when applied to particular mach ...
... and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when applied to particular mach ...