Two important problems in preference elicitation are rank aggregation and label ranking. Rank aggregation consists of finding a ranking that best summarizes a collection of preferences of some agents. The latter, label ranking, aims at learning a mapping between data instances and rankings defined over a finite set of categories or labels. This problem can effectively model many real application scenarios such as recommender systems. However, even when the preferences of populations usually change over time, related literature has so far addressed both problems over non-evolving preferences. This work deals with the problems of rank aggregation and label ranking over non-stationary data streams. In this context, there is a set of $n$ items and $m$ agents which provide their votes by casting a ranking of the $n$ items. The rankings are noisy realizations of an unknown probability distribution that changes over time. Our goal is to learn, in an online manner, the current ground truth distribution of rankings. We begin by proposing an aggregation function called Forgetful Borda (FBorda) that, using a forgetting mechanism, gives more importance to recently observed preferences. We prove that FBorda is a consistent estimator of the Kemeny ranking and lower bound the number of samples needed to learn the distribution while guaranteeing a certain level of confidence. Then, we develop a $k$-nearest neighbor classifier based on the proposed FBorda aggregation algorithm for the label ranking problem and demonstrate its accuracy in several scenarios of label ranking problem over evolving preferences.

We study a family of sparse estimators defined as minimizers of some empirical Lipschitz loss function---which include hinge, logistic and quantile regression losses---with a convex, sparse or group-sparse regularization. In particular, we consider the L1-norm on the coefficients, its sorted Slope version, and the Group L1-L2 extension. First, we propose a theoretical framework which simultaneously derives new L2 estimation upper bounds for all three regularization schemes. For L1 and Slope regularizations, our bounds scale as $(k^*/n) \log(p/k^*)$---$n\times p$ is the size of the design matrix and $k^*$ the dimension of the theoretical loss minimizer $\beta^*$---matching the optimal minimax rate achieved for the least-squares case. For Group L1-L2 regularization, our bounds scale as $(s^*/n) \log\left( G / s^* \right) + m^* / n$---$G$ is the total number of groups and $m^*$ the number of coefficients in the $s^*$ groups which contain $\beta^*$---and improve over the least-squares case. We additionally show that Group L1-L2 is superior to L1 and Slope when the signal is strongly group-sparse. Our bounds are achieved both in probability and in expectation, under common assumptions in the literature. Second, we propose an accelerated proximal algorithm which computes the convex estimators studied when the number of variables is of the order of $100,000$. We compare the statistical performance of our estimators against standard baselines for settings where the signal is either sparse or group-sparse. Our experiments findings reveal (i) the good empirical performance of L1 and Slope for sparse binary classification problems, (ii) the superiority of Group L1-L2 regularization for group-sparse classification problems and (iii) the appealing properties of sparse quantile regression estimators for sparse regression problems with heteroscedastic noise.

Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to data noise in experiment data, large scale of GRNs, and inconsistency between gene expression profiles and function modules, etc. It is promising to semi-supervise this process by prior information, but shortage of prior information sometimes make it very challenging. Meanwhile, it is also annoying, and sometimes impossible to discovery gold standard for evaluation of clustering results.\\ Results: With assistance of an online enrichment tool, this research proposes a semi-supervised hierarchical clustering method via deconvolved correlation matrix~(SHC-DC) to discover interaction clusters of large-scale GRNs. Three benchmark networks including a \emph{Ecoli} network and two \emph{Yeast} networks are employed to test semi-supervision scheme of the proposed method. Then, SHC-DC is utilized to cluster genes in sleep study. Results demonstrates it can find interaction modules that are generally enriched in various signal pathways. Besides the significant influence on blood level of interleukins, impact of sleep on important pathways mediated by them is also validated by the discovered interaction modules.

A dictionary is a database of standard vectors, so that other vectors / signals are expressed as linear combinations of dictionary vectors, and the task of learning a dictionary for a given data is to find a good dictionary so that the representation of data points has desirable features. Dictionary learning and the related matrix factorization methods have gained significant prominence recently due to their applications in Wide variety of fields like machine learning, signal processing, statistics etc. In this article we study the dictionary learning problem for achieving desirable features in the representation of a given data with almost sure recovery constraints. We impose the constraint that every sample is reconstructed properly to within a predefined threshold. This problem formulation is more challenging than the conventional dictionary learning, which is done by minimizing a regularised cost function. We make use of the duality results for linear inverse problems to obtain an equivalent reformulation in the form of a convex-concave min-max problem. The resulting min-max problem is then solved using gradient descent-ascent like algorithms.

A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models, classifiers, loss functions). Over the past decade, coreset construction algorithms have been suggested for many fundamental problems in e.g. machine/deep learning, computer vision, graphics, databases, and theoretical computer science. This introductory paper was written following requests from (usually non-expert, but also colleagues) regarding the many inconsistent coreset definitions, lack of available source code, the required deep theoretical background from different fields, and the dense papers that make it hard for beginners to apply coresets and develop new ones. The paper provides folklore, classic and simple results including step-by-step proofs and figures, for the simplest (accurate) coresets of very basic problems, such as: sum of vectors, minimum enclosing ball, SVD/ PCA and linear regression. Nevertheless, we did not find most of their constructions in the literature. Moreover, we expect that putting them together in a retrospective context would help the reader to grasp modern results that usually extend and generalize these fundamental observations. Experts might appreciate the unified notation and comparison table that links between existing results. Open source code with example scripts are provided for all the presented algorithms, to demonstrate their practical usage, and to support the readers who are more familiar with programming than math.

Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.

It is estimated to top. Therefore the best way to understand machine learning is to look at some example problems. Machine Learning Ex2 - Linear Regression Implementing linear regression using gradient descent in Scala based on Andrew Ng's machine learning course. In this book, you'll do exactly that. Quality Assurance in Software Testing: Prevention is better than a cure, even where it concerns software solutions. HP Elite x2 Designed for IT, loved by users. With this mind, the Machine Learning & AI For Upstream Onshore Oil & Gas 2019 purely focuses on understanding the profitable applications of Machine Learning and AI, primarily for optimizing production for onshore E&Ps, and examine how to improve operational efficiencies in drilling and completions.

Data science or applied statistics courses typically start with linear models, but in its way, K-nearest neighbors is probably the simplest widely used model conceptually. KNN models are really just technical implementations of a common intuition, that things that share similar features tend to be, well, similar. This is hardly a deep insight, yet these practical implementations can be extremely powerful, and, crucially for someone approaching an unknown dataset, can handle non-linearities without any complicated data-engineering or model set up. As an illustrative example, let's consider the simplest case of using a KNN model as a classifier. Let's say you have data points that fall into one of three classes.

At present, machine learning, artificial intelligence, and data science are the most booming factor to bring the next revolution in this industrial and technology-driven world. Therefore, there are a significant number of opportunities that are waiting for fresh graduate data scientists and machine learning developers to apply their specific knowledge in a particular domain. However, it's not that easy as you are thinking. The interview procedure that you will have to go through will definitely be very challenging, and you will have hard competitors. Moreover, your skill will be tested in different ways, i.e., technical and programming skills, problem-solving skills, and your ability to apply machine learning techniques efficiently and effectively, and your overall knowledge about machine learning. To help you with your upcoming interview, in this post, we have listed frequently asked machine learning interview questions. Traditionally, to recruit a machine learning developer, several types of machine learning interview questions are asked. Firstly, some basic machine learning questions are asked. Then, machine learning algorithms, their comparisons, benefits, and drawbacks are asked. Finally, the problem-solving skill using these algorithms and techniques are examined.

Algorithms are often grouped by similarity in terms of their function (how they work). For example, tree-based methods, and neural network inspired methods. I think this is the most useful way to group algorithms and it is the approach we will use here. This is a useful grouping method, but it is not perfect. There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method.