We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained ap- proximations. Rather intriguingly, all three techniques can be viewed as instantiations of the following idea: replace the kernel function by a "randomized kernel" which behaves like

In this guide, I will share a detailed deep-dive of what is sampling, what are sampling techniques, and the industry use cases. As you know, fundamental to Data Science is getting good quality sample data. We always derive population parameters from the sample. Our machine learning models will not yield the desired results, if the sample data we worked on does not closely represent the population. In sampling, we select a group of individuals from a target population. This group of individuals forms a sample.

Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies within the code and is associated with an issue which is prominent to experienced software developers with acceptable coding practices. Recent studies have often observed that codes having code smells are often prone to a higher probability of change in the software development cycle. In this paper, we developed code smell prediction models with the help of features extracted from source code to predict eight types of code smell. Our work also presents the application of data sampling techniques to handle class imbalance problem and feature selection techniques to find relevant feature sets. Previous studies had made use of techniques such as Naive - Bayes and Random forest but had not explored deep learning methods to predict code smell. A total of 576 distinct Deep Learning models were trained using the features and datasets mentioned above. The study concluded that the deep learning models which used data from Synthetic Minority Oversampling Technique gave better results in terms of accuracy, AUC with the accuracy of some models improving from 88.47 to 96.84.

Here's a scenario I'm sure you are familiar with. You download a relatively big dataset and are excited to get started with analyzing it and building your machine learning model. And snap – your machine gives an "out of memory" error while trying to load the dataset. It's happened to the best of us. It's one of the biggest hurdles we face in data science – dealing with massive amounts of data on computationally limited machines (not all of us have Google's resource power!).

The aim of General Game Playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. One of the main challenges such agents face is to automatically learn knowledge-based heuristics in real-time, whether for evaluating game positions or for search guidance. In recent years, GGP agents that use Monte-Carlo simulations to reason about their actions have become increasingly more popular. For competitive play such an approach requires an effective search-control mechanism for guiding the simulation playouts. In here we introduce several schemes for automatically learning search guidance based on both statistical and reinforcement learning techniques. We compare the different schemes empirically on a variety of games and show that they improve significantly upon the current state-of-the-art in simulation-control in GGP. For example, in the chess-like game Skirmish, which has proved a particularly challenging game for simulation-based GGP agents, an agent employing one of the proposed schemes achieves 97% winning rate against an unmodified agent.

We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations. Rather intriguingly, all three techniques can be viewed as instantiations of the following idea: replace the kernel function by a "randomized kernel" which behaves like in expectation.

We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations. Rather intriguingly, all three techniques can be viewed as instantiations of the following idea: replace the kernel function by a "randomized kernel" which behaves like in expectation.

We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations. Ratherintriguingly, all three techniques can be viewed as instantiations of the following idea: replace the kernel function by a "randomized kernel" which behaves like in expectation.