From Cutting Planes Algorithms to Compression Schemes and Active Learning

Cutting-plane methods are well-studied localization(and optimization) algorithms. We show that they provide a natural framework to perform machinelearning ---and not just to solve optimization problems posed by machinelearning--- in addition to their intended optimization use. In particular, theyallow one to learn sparse classifiers and provide good compression schemes.Moreover, we show that very little effort is required to turn them intoeffective active learning methods. This last property provides a generic way todesign a whole family of active learning algorithms from existing passivemethods. We present numerical simulations testifying of the relevance ofcutting-plane methods for passive and active learning tasks.Comment: IJCNN 2015, Jul 2015, Killarney, Ireland. 2015, \<http://www.ijcnn.org/\&g

BOOSTING

• Research ways to efficiently implement machinelearning algorithms on MIPS/PowerVR • Research possible extensions to MIP

Extended Abstract: Analysis of 1000 Arbiter PUF based RFID Tags

In this extended abstract a large-scale analysis of 4- way Arbiter PUFs is performed with measurement results from 1000 RFID tags. Arbiter PUFs are one of the most important building blocks in PUF-based protocols and have been the subject of many papers. However, in the past often only software simulations or a limited number of test chips were available for analysis. Therefore, the goal of this work is to verify earlier findings in regard to the uniqueness and reliability of Arbiter PUFs by using a much larger measurement set. Furthermore, we used machine learning algorithms to approximate and compare the internal delay differences of the employed PUF. One of the main research questions in this paper is to examine if any “outliers” occurred, i.e., if some tags performed considerably different. This might for example happen due to some unusual manufacturing variations or faults. However, our findings are that for all of the analyzed tags the parameters fell within the range of a Gaussian distribution without significant outliers. Hence, our results are indeed in line with the results of prior work

Experiment Databases: Creating a New Platform for Meta-Learning Research

Many studies in machine learning try to investigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning tasks and evaluation procedures continue to emerge in the literature. Thus, it is impossible for a single study to cover this expanding space of learning approaches. In this paper, we propose a community-based approach for the analysis of learning algorithms, driven by sharing meta-data from previous experiments in a uniform way. We illustrate how organizing this information in a central database can create a practical public platform for any kind of exploitation of meta-knowledge, allowing effective reuse of previous experimentation and targeted analysis of the collected results

An empirical analysis of Brazilian courts law documents using learning techniques

This paper describes a survey on investigating judicial data to find patterns and relations between crime attributes and corresponding decisions made by courts, aiming to find import directions that interpretation of the law might be taking. We have developed an initial methodology and experimentation to look for behaviour patterns to build judicial sentences in the scope of Brazilian criminal courts and achieved results related to important trends in decision making. Neural networks-based techniques were applied for classification and pattern recognition, based on Multi-Layer Perceptron and Radial-basis Functions, associated with data organisation techniques and behavioral modalities extractio

Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machinelearning and statistics are considered and applied to the customerchoice modeling problem. The application of ensemble learningusually improves the prediction quality of flexible models likedecision trees and thus leads to improved predictions. We giveexperimental results for two real-life marketing datasets usingdecision trees, ensemble versions of decision trees and thelogistic regression model, which is a standard approach for thisproblem. The ensemble models are found to improve upon individualdecision trees and outperform logistic regression.Next, an additive decomposition of the prediction error of amodel, the bias/variance decomposition, is considered. A modelwith a high bias lacks the flexibility to fit the data well. Ahigh variance indicates that a model is instable with respect todifferent datasets. Decision trees have a high variance componentand a low bias component in the prediction error, whereas logisticregression has a high bias component and a low variance component.It is shown that ensemble methods aim at minimizing the variancecomponent in the prediction error while leaving the bias componentunaltered. Bias/variance decompositions for all models for bothcustomer choice datasets are given to illustrate these concepts.brand choice;data mining;boosting;choice models;Bias/Variance decomposition;Bagging;CART;ensembles

Accelerating federated learning via momentum gradient descent

Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize first-order gradient descent (GD) and do not consider the preceding iterations to gradient update which can potentially accelerate convergence. In this article, we consider momentum term which relates to the last iteration. The proposed momentum federated learning (MFL) uses momentum gradient descent (MGD) in the local update step of FL system. We establish global convergence properties of MFL and derive an upper bound on MFL convergence rate. Comparing the upper bounds on MFL and FL convergence rates, we provide conditions in which MFL accelerates the convergence. For different machine learning models, the convergence performance of MFL is evaluated based on experiments with MNIST and CIFAR-10 datasets. Simulation results confirm that MFL is globally convergent and further reveal significant convergence improvement over FL

A hybrid representation based simile component extraction

Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models