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 frame is received that has the wideband audio signal. The low band audio signal is encoded to generate an encoded low band signal. The high band signal is analyzed to determine whether the high band signal is perceptually relevant to the low band signal. If the high band signal is not perceptually relevant to the low band signal, the low band signal is encoded and provided in a frame to the decoder without including parameters corresponding to characteristics of the high band signal. If the high band signal is perceptually relevant, the high band signal is encoded to generate an encoded high band signal. The resultant frame that is sent to the decoder will include a combination of the encoded low band signal and the encoded high band signal.

Split-band speech compression based on loudness estimation

The potential use of third-order statistics (TOS) in speech processing applications is examined. A source-system speech representation and analysis algorithm is considered. It computes the autoregressive (AR) parameters of speech from its third-order cumulants. The TOS-based AR estimates are compared with those obtained using second order statistics. Results are given voiced and unvoiced speech segments which are corrupted by additive Gaussian noise. The TOS-based AR estimation algorithm is also used to estimate the parameters of the short-term prediction filter of a code excited linear prediction (CELP) algorithm. >

Speech processing using higher order statistics

Although considerable effort has been devoted to the problem of reconstructing compressively sensed video, no existing algorithm achieves results comparable to commonly available video compression methods such as H.264. One possible avenue for improving compressively sensed video reconstruction is the use of optical flow information. Current efforts reported in the literature have not fully utilized optical flow information, instead focusing on limited cases such as stationary backgrounds with sparse foreground motion. In this paper, a reconstruction method is presented which fully utilizes optical flow information to increase the quality of reconstruction. The special cases of known image motion and constant global image motion are presented, and the performance of the algorithm on existing datasets is evaluated.

Optical flow for compressive sensing video reconstruction

A hybrid transform method for analysis/synthesis of speech

Sparse representations with learned dictionaries have been successful in several image analysis applications. In this paper, we propose and analyze the framework of ensemble sparse models, and demonstrate their utility in image restoration and unsupervised clustering. The proposed ensemble model approximates the data as a linear combination of approximations from multiple \textit{weak} sparse models. Theoretical analysis of the ensemble model reveals that even in the worst-case, the ensemble can perform better than any of its constituent individual models. The dictionaries corresponding to the individual sparse models are obtained using either random example selection or boosted approaches. Boosted approaches learn one dictionary per round such that the dictionary learned in a particular round is optimized for the training examples having high reconstruction error in the previous round. Results with compressed recovery show that the ensemble representations lead to a better performance compared to using a single dictionary obtained with the conventional alternating minimization approach. The proposed ensemble models are also used for single image superresolution, and we show that they perform comparably to the recent approaches. In unsupervised clustering, experiments show that the proposed model performs better than baseline approaches in several standard datasets.

Andreas Spanias

Papers

Split-band speech compression based on loudness estimation

Speech processing using higher order statistics

Optical flow for compressive sensing video reconstruction

A hybrid transform method for analysis/synthesis of speech

Ensemble Sparse Models for Image Analysis