Data Mining Practical Machine Learning Tools and Techniques

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.

Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book. The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.

Foundations of Machine Learning

/pdf/neural-networks-and-deep-learning-3ccs3ain8p.pdf

Neural Networks and Deep Learning

We consider the problem of multiclass classification. Our main thesis is that a simple "one-vs-all" scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. This thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments.

/pdf/in-defense-of-one-vs-all-classification-mgtoa8ot9w.pdf

In Defense of One-Vs-All Classification

The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a ray-tracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beam-selection techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology.

5G MIMO Data for Machine Learning: Application to Beam-Selection Using Deep Learning

Computational Processing of the Portuguese Language

Millimeter wave communication systems can leverage information from sensors to reduce the overhead associated with link configuration. LIDAR (light detection and ranging) is one sensor widely used in autonomous driving for high resolution mapping and positioning. This paper shows how LIDAR data can be used for line-of-sight detection and to reduce the overhead in millimeter wave beam-selection. In the proposed distributed architecture, the base station broadcasts its position. The connected vehicle leverages its LIDAR data to suggest a set of beams selected via a deep convolutional neural network. Co-simulation of communications and LIDAR in a vehicle-to-infrastructure (V2I) scenario confirm that LIDAR can help configuring mmWave V2I links.

LIDAR Data for Deep Learning-Based mmWave Beam-Selection

Establishing and tracking beams in millimeter-wave (mmWave) vehicular communication is a challenging task. Large antenna arrays and narrow beams introduce significant system overhead configuring the beams using exhaustive beam search. In this paper, we propose to learn the optimal beam pair index by exploiting the locations and types of the receiver vehicle and its neighboring vehicles (situational awareness), leveraging machine learning classification and past beam training data. We formulate the mmWave beam selection as a multi-class classification problem based on hand-crafted features that capture the situational awareness in different coordinates. We then provide a comprehensive comparison of the different classification models and various levels of situational awareness. Furthermore, we examine several practical issues in the implementation: localization is susceptible to inaccuracy; situational awareness at the base station (BS) can be outdated due to vehicle mobility and limited location reporting frequencies; the situational awareness may be incomplete since vehicles could be invisible to the BS if they are not connected. To demonstrate the scalability of the proposed beam selection solution in the large antenna array regime, we propose two solutions to recommend multiple beams and exploit an extra phase of beam sweeping among the recommended beams. The numerical results show that situational awareness-assisted beam selection using machine learning is able to provide beam prediction, with accuracy that increases with more complete knowledge of the environment.

/pdf/mmwave-vehicular-beam-selection-with-situational-awareness-56qkmsvpgs.pdf

Aldebaro Klautau

Papers

In Defense of One-Vs-All Classification

5G MIMO Data for Machine Learning: Application to Beam-Selection Using Deep Learning

Computational Processing of the Portuguese Language

LIDAR Data for Deep Learning-Based mmWave Beam-Selection

MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning