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

Exploiting known semantic relationships between fine-grained tasks is critical to the success of recent model agnostic approaches. These approaches often rely on meta-optimization to make a model robust to systematic task or domain shifts. However, in practice, the performance of these methods can suffer, when there are no coherent semantic relationships between the tasks (or domains). We present Invenio, a structured meta-learning algorithm to infer semantic similarities between a given set of tasks and to provide insights into the complexity of transferring knowledge between different tasks. In contrast to existing techniques such as Task2Vec and Taskonomy, which measure similarities between pre-trained models, our approach employs a novel self-supervised learning strategy to discover these relationships in the training loop and at the same time utilizes them to update task-specific models in the meta-update step. Using challenging task and domain databases, under few-shot learning settings, we show that Invenio can discover intricate dependencies between tasks or domains, and can provide significant gains over existing approaches in terms of generalization performance. The learned semantic structure between tasks/domains from Invenio is interpretable and can be used to construct meaningful priors for tasks or domains.

Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning

The study of the behavior of ion-channels can provide significant information to detect metal ions and small organic molecules in solution. Discrimination of different analytes can be performed by extracting appropriate features from the ion-channel signals and using them for classification. In this paper, we consider features extracted from the Fourier, Wavelet and Walsh-Hadamard domain representations of the ion-channel signals. The proposed approach uses the power distribution information in the transform domains as features for discrimination. We compare the performance of all the three sets of features using support vector machines for classification of analytes and present the results. Results obtained show that the transform domain features achieve high classification rates in addition to high sensitivity and specificity rates.

Transform domain features for ion-channel signal classification using support vector machines

The authors study some problems associated with the implementation of a frequency-domain adaptive algorithm on a fixed-point signal processor. In particular, they propose methods to improve the convergence speed and reduce the computational complexity of a constrained frequency-domain algorithm that uses a time-varying step size. In addition, they study the effects of finite word length and fixed-point arithmetic. Improvements are realized by adopting a novel data reusing scheme and by applying running and pruned FFTs (fast Fourier transforms). Results are given using synthetic data as well as data from noise cancellation experiments. >

Real-time implementation of a frequency-domain adaptive filter on a fixed-point signal processor

We analyze the performance of an uncoded orthogonal frequency-division multiplexing (OFDM) system in a quasistatic fading channel where optimal training is being employed for the acquisition of channel state information (CSI). A bit-error probability (BEP) expression is obtained for the linear minimum mean-square error (LMMSE) based channel estimator where the mean-square error (MSE) of the channel estimator is minimized by equispaced, equipowered training with the number of training tones equal to the channel length. Optimal power allocation is obtained by minimizing the BEP expressions. The performances of BPSK, QPSK and 16-QAM schemes are considered. It is shown that while equal power training performs around 3 dB worse than the known channel case, our optimal power channel estimator performance varies between these two, depending on the number of subcarrier to channel length ratio. Numerical examples are presented to further clarify and confirm the analytical findings.

On the performance of optimal training-based OFDM with channel estimation error

Examines the performance of adaptive algorithms such as gradient, dead-zone, and bounding ellipsoid in the presence of bounded disturbances. The authors begin with a simple bursting example and continue by describing the algorithms and their properties, including analytical results on their asymptotic performance. Finally, a new algorithm is proposed, providing both MSE and maximum steady-state error performance guarantees.

Andreas Spanias

Papers

Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning

Transform domain features for ion-channel signal classification using support vector machines

Real-time implementation of a frequency-domain adaptive filter on a fixed-point signal processor

On the performance of optimal training-based OFDM with channel estimation error

System identification based on bounded error constraints