Machine-learning algorithms are the future of credit scoring Billtrust


In order to create credit scores that provide utility and value, Credit2b's scores are on a scale of 0-100, with each point on this scale representing the probability of a positive outcome for the score. Therefore, a score of 80 simply means that there is an 80% probability that the company will pay on time for example. Other ratings agency scores are described in either tiers or bands that often cause confusion for credit practitioners who need to make important decisions quickly. With the tiered approach, two very similar companies may be scored in separate bands with completely different interpretations due to the randomness of the bands, and the simplicity of their data analysis algorithms. Machine-learning solutions deal with continuum, and give our customers information they can process and use quickly.

Machine Learning is Fun


Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code.

PAC-Bayes bounds for stable algorithms with instance-dependent priors

Neural Information Processing Systems

PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our paper is using priors defined in terms of the data-generating distribution. Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients.

Infographic: Machine learning basics with algorithm examples


Use this easy-to-understand, downloadable infographic overview of machine learning basics to identify the popular algorithms used to answer common machine learning questions. Algorithm examples help the machine learning beginner understand which algorithms to use and what they are used for. Azure Machine Learning Studio comes with a large number of machine learning algorithms that you can use to solve predictive analytics problems. The downloadable infographic below demonstrates how the four types of machine learning algorithms - regression, anomaly detection, clustering, and classification - can be used to answer your machine learning questions. Get the most out of the infographic by downloading it - the PDF has links to examples of each algorithm.

van Hasselt

AAAI Conferences

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.