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

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Convex Analysisの二,三の進展について

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

Statistical Analysis with Missing Data

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Embodiments relate to facilitating a meeting. A method for facilitating a meeting of a group of participants is provided. The method generates a graph of words from speeches of the participants as the words are received from the participants. The method partitions the group of participants into a plurality of subgroups of participants. The method performs a graphical text analysis on the graph to identify a cognitive state for each participant and a cognitive state for each subgroup of participants. The method informs at least one of the participants about the identified cognitive state of a participant or a subgroup of participants.

Facilitating a meeting using graphical text analysis

Embodiments relate to facilitating communications between customers and customer service representatives. A method for facilitating communications between customers and customer service representatives is provided. The method generates a graph of expressions of a customer. The method generates a graph of expressions of each customer service representative of a plurality of customer service representatives. The method performs a graphical text analysis on the graph for the customer to identify an interaction style of the customer. The method performs a graphical text analysis on the graph for each customer service representative to identify an interaction style of the customer service representative. The method selects a customer service representative from the plurality of the customer service representatives based on the interaction style of the customer and the interaction styles of the plurality of the customer service representative. The method starts a communication between the customer and the selected customer service representative.

Using graphical text analysis to facilitate communication between customers and customer service representatives

The invention concerns apparatus and methods that determine availability and performance of entities providing services in a distributed system using filtered service-consumer feedback In particular, apparatus and methods of the invention filter service-consumer feedback in order to reduce the effect of circumstances unique to individual service consumers or to groups of service consumers that do not accurately reflect the actual availability or performance of service-providing entities In this way an accurate appraisal is gained regarding the performance and availability of a service-providing entity Reactive methods of the invention can be combined with proactive methods such as, for example, active status probing, to further improve the accuracy of data concerning the status and availability of service-providing entities

Apparatus and Methods for Determining Availability and Performance of Entities Providing Services in a Distributed System Using Filtered Service Consumer Feedback

Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art methods rely on error backpropagation, which suffers from several well-known issues, such as vanishing and exploding gradients, inability to handle non-differentiable nonlinearities and to parallelize weight-updates across layers, and biological implausibility. These limitations continue to motivate exploration of alternative training algorithms, including several recently proposed auxiliary-variable methods which break the complex nested objective function into local subproblems. However, those techniques are mainly offline (batch), which limits their applicability to extremely large datasets, as well as to online, continual or reinforcement learning. The main contribution of our work is a novel online (stochastic/mini-batch) alternating minimization (AM) approach for training deep neural networks, together with the first theoretical convergence guarantees for AM in stochastic settings and promising empirical results on a variety of architectures and datasets.

Beyond Backprop: Alternating Minimization with co-Activation Memory.

This chapter contains sections titled: 1 Pseudo-ROC Prediction Versus Reproducibility (P, R) Curves, 2 Single-Subject (P, R) Curves, 3 Multisubject (P, R) Curves, 4 Multisubject Prediction Heterogeneity, 5 Discussion, 6 Summary, Note, References

Irina Rish

Papers

Facilitating a meeting using graphical text analysis

Using graphical text analysis to facilitate communication between customers and customer service representatives

Apparatus and Methods for Determining Availability and Performance of Entities Providing Services in a Distributed System Using Filtered Service Consumer Feedback

Beyond Backprop: Alternating Minimization with co-Activation Memory.

Stability and Reproducibility in fMRI Analysis