Breast cancer kills a large number of women around the world. Infrared thermography is a promising screening technique which does not involve harmful radiation for the patient and has a relatively low cost. This work proposes an approach for classifying patients into three different classes using infrared images: healthy patients, patients with benign changes and patients with cancer (malignant changes). A set of features is extracted from each image and two approaches are used in the classification process. The first is based on Artificial Neural Networks while the second is based on Support Vector Machines.

Check out my new paper entitled "Support Vector Machines, Wasserstein's distance and gradient-penalty GANs are connected"! In this paper, we explain how one can derive SVMs and gradient penalized GANs (or those with Lipschitz-1 discriminator) from the same framework! We also show new gradient penalties that lead to better GANs. This paper may completely change your perspective on the Wasserstein's distance, Wasserstein GAN (WGAN), Hinge GAN (HingeGAN), and the use of gradient penalties in GANs. At least, it did for me!

In this course series we will discuss how to create and assess machine learning models. Students must have knowledge of basic statistics and basic linear algebra and Python programming. Please attend all classes within the series (101 and 102) and consider bringing a laptop. This program is in partnership with Princeton School of AI. Registration is limited to 30.

We generalize the concept of maximum-margin classifiers (MMCs) to arbitrary norms and non-linear functions. Support Vector Machines (SVMs) are a special case of MMC. We find that MMCs can be formulated as Integral Probability Metrics (IPMs) or classifiers with some form of gradient norm penalty. This implies a direct link to a class of Generative adversarial networks (GANs) which penalize a gradient norm. We show that the Discriminator in Wasserstein, Standard, Least-Squares, and Hinge GAN with Gradient Penalty is an MMC. We explain why maximizing a margin may be helpful in GANs. We hypothesize and confirm experimentally that $L^\infty$-norm penalties with Hinge loss produce better GANs than $L^2$-norm penalties (based on common evaluation metrics). We derive the margins of Relativistic paired (Rp) and average (Ra) GANs.

Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data. Previous feature selection methods usually cannot keep more local structure information. To address the defects previously mentioned, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objection function can be solved by employing generalized power iteration (GPI) and augmented Lagrangian multiplier (ALM) methods. Experimental results show that the proposed method can more effectively reduce the feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is proved as well. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.

The article explains the SVM algorithm in an easy way. Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In simple terms development of algorithms which enable the machine to learn and perform tasks and activities. Over a period of time, many techniques and methodologies were developed for machine learning tasks. In this article, we are going to learn almost everything about one such supervised machine learning algorithm which can be used for both classification and regression(SVR) i.e.

Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive dynamic visualizations Use SciKit-Learn for Machine Learning Tasks Random Forest and Decision Trees Natural Language Processing and Spam Filters Support Vector Machines Some programming experience Admin permissions to download files Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.

WARNING: to avoid counterfeit, make sure that the book ships from and sold by Amazon. Statistical Learning, with Applications in R: "This is a compact "how to do data science" manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend "The Hundred-Page Machine Learning Book" for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."

During my internship this summer, I built a multi-party computation (MPC) tool that implements a 3-party computation protocol for perceptron and support vector machine (SVM) algorithms. MPC enables multiple parties to perform analyses on private datasets without sharing them with each other. I developed a technique that lets three parties obtain the results of machine learning across non-public datasets. It is now possible to perform data analytics on private datasets that was previously impossible due to data privacy constraints. For MPC protocols, a group of parties, each with their own set of secret data, xi, share an input function, f, and each is able to obtain the output of f(x1,…,xn) without learning the private data of other parties.