Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

http://www.csun.edu/~andrzej/COMP529-S05/papers/Mesh%20Networks%20Survey.pdf

Wireless mesh networks: a survey

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

A sensor network is used for distributed SNR estimation. Sensors observe a signal embedded in noise. These observations are phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center. At the fusion center, the location parameter and scale parameter are estimated using a minimum-variance estimator as well as computationally simple alternatives. Performance evaluated using the asymptotic variance of the estimators shows that the computationally simple estimators have the same asymptotic performance as the minimum-variance estimators. The optimal phase transmission parameters are also determined for different sensing noise distributions. Simulation results corroborate analytical results.

Distributed SNR estimation using constant modulus signaling over Gaussian multiple-access channels

We propose a probabilistic generative model for extracting intended path shape qualities of an object moving under human control in real time. At each instant, we decide whether the object is moving in a straight, curved, or random path, or whether it has stopped moving. Our model incorporates sensor noise as well as human imperfections in the intended motion. As well as tracking the object's position, velocity, and motion direction, we compute the posterior probability of each shape quality hypothesis given all sensed-data in the horizon [t - N + 1,t]; the hypothesis maximizing this posterior is taken as the decision. The posterior is computed using the unscented Kalman filter (UKF), as our model is inherently nonlinear. The path-shape quality tracking is successfully embedded in a hybrid physical-digital interface where the position of an illuminated ball, sensed by a low-cost video camera array, triggers multimodal feedback in a mediated learning environment. We show successful results on a variety of real-world motion paths where the participant is given only verbal descriptions of how to move. Our generative model is further validated by user studies involving a simple color-based interaction, where participants discover shape quality controls as they interact.

Tracking the Path Shape Qualities of Human Motion

Sequence alignment is the positioning of primary biological sequences, such as DNA, RNA and protein sequences, to identify regions of similarity in large databases. Common signal processing techniques include cross-correlations in time or frequency. However, these techniques can result in many misalignments when capturing a grouping in local or repetitive portions of the sequence. We propose a time-frequency based alignment technique using the matching pursuit decomposition method and a mapping algorithm. The aim of this alignment technique is to identify local and global alignments more efficiently and with greater precision than existing methods. Its success is based on the fact that sequence elements are mapped to unique Gaussian basis atoms that uniformly sample the time-frequency plane.

DNA sequence alignment using the matching pursuit decomposition

A fast frequency-domain IIR (infinite-impulse response) algorithm is proposed called the frequency-domain optimal block adaptive recursive algorithm (FOBAR). This is the frequency-domain implementation of the recently proposed time domain optimal block adaptive recursive algorithm (OBAR). The FOBAR requires fewer computations than the OBAR for high-order filters. Simulations are given for both real-life and synthetic signals. In particular, FOBAR was used successfully for high-order system identification, adaptive noise cancellation, and pole-zero prediction. >

Fast frequency domain implementation of a block IIR filter with application

The engineering, arts and science disciplines involved in media training, research and education at Arizona State University have come together to create the Arts, Media and Engineering (AME) graduate education and research program. The education component of this program consists of formalized graduate concentrations within existing degrees that allow faculty and students to combine extensive training in their chosen discipline offered through their home department with hybrid engineering-arts-sciences training offered through AME. This paper states a basic education and training problem in arts and media and presents a program that was formed at Arizona State University to address this problem. The structures, participation, associated sub-areas of this program are also described.

https://ame2.asu.edu/faculty/hs/pubs/2005/2005-362_Final-spanias-asee.pdf

Andreas Spanias

Papers

Distributed SNR estimation using constant modulus signaling over Gaussian multiple-access channels

Tracking the Path Shape Qualities of Human Motion

DNA sequence alignment using the matching pursuit decomposition

Fast frequency domain implementation of a block IIR filter with application

An Interdisciplinary Arts And Engineering Initiative For Experiential Multimedia