• Mach. Learn. (IF 2.672) Pub Date : 2020-07-02
Sunwoo Han, Hyunjoong Kim, Yung-Seop Lee

Random forest (RF) is one of the most popular parallel ensemble methods, using decision trees as classifiers. One of the hyper-parameters to choose from for RF fitting is the nodesize, which determines the individual tree size. In this paper, we begin with the observation that for many data sets (34 out of 58), the best RF prediction accuracy is achieved when the trees are grown fully by minimizing

更新日期：2020-07-03
• Mach. Learn. (IF 2.672) Pub Date : 2020-06-28
Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja

Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single

更新日期：2020-06-28
• Mach. Learn. (IF 2.672) Pub Date : 2020-06-10
Adriano Rivolli, Jesse Read, Carlos Soares, Bernhard Pfahringer, André C. P. L. F. de Carvalho

Investigating strategies that are able to efficiently deal with multi-label classification tasks is a current research topic in machine learning. Many methods have been proposed, making the selection of the most suitable strategy a challenging issue. From this premise, this paper presents an extensive empirical analysis of the binary transformation strategies and base algorithms for multi-label learning

更新日期：2020-06-10
• Mach. Learn. (IF 2.672) Pub Date : 2020-06-04
Konstantinos Sechidis, Laura Azzimonti, Adam Pocock, Giorgio Corani, James Weatherall, Gavin Brown

There was a mistake in the proof of the optimal shrinkage intensity for our estimator presented in Section 3.1.

更新日期：2020-06-04
• Mach. Learn. (IF 2.672) Pub Date : 2020-06-03
Guillaume Lecué, Matthieu Lerasle, Timothée Mathieu

There is a mistake in one of the authors’ names (in both online and print versions of the article): it should be Timothée Mathieu instead of Timlothée Mathieu.

更新日期：2020-06-03
• Mach. Learn. (IF 2.672) Pub Date : 2020-05-31
Tomoharu Iwata, Machiko Toyoda, Shotaro Tora, Naonori Ueda

We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measure the performance with inexact anomaly labels, we define the inexact AUC, which is

更新日期：2020-05-31
• Mach. Learn. (IF 2.672) Pub Date : 2020-05-11
Rodrigo Azevedo Santos, Aline Paes, Gerson Zaverucha

Statistical machine learning algorithms usually assume the availability of data of considerable size to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data by relying on a model learned in a source domain where data is easy to obtain to be a starting point

更新日期：2020-05-11
• Mach. Learn. (IF 2.672) Pub Date : 2020-04-27
Guillaume Lecué, Matthieu Lerasle, Timlothée Mathieu

We present an extension of Chervonenkis and Vapnik’s classical empirical risk minimization (ERM) where the empirical risk is replaced by a median-of-means (MOM) estimator of the risk. The resulting new estimators are called MOM minimizers. While ERM is sensitive to corruption of the dataset for many classical loss functions used in classification, we show that MOM minimizers behave well in theory,

更新日期：2020-04-27
• Mach. Learn. (IF 2.672) Pub Date : 2020-04-23
Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles. In order to use machine learning in a safety-critical system, it is necessary to demonstrate the safety and security of the system through engineering processes. However, thus far, no such widely accepted engineering concepts

更新日期：2020-04-24
• Mach. Learn. (IF 2.672) Pub Date : 2020-04-02
Jessa Bekker, Jesse Davis

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2020-03-31
Artür Manukyan, Elvan Ceyhan

We employ random geometric digraphs to construct semi-parametric classifiers. These data-random digraphs belong to parameterized random digraph families called proximity catch digraphs (PCDs). A related geometric digraph family, class cover catch digraph (CCCD), has been used to solve the class cover problem by using its approximate minimum dominating set and showed relatively good performance in the

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2020-03-16
Sarang Kapoor, Dhish Kumar Saxena, Matthijs van Leeuwen

Over the past decade, network analysis has attracted substantial interest because of its potential to solve many real-world problems. This paper lays the conceptual foundation for an application in aviation, through focusing on the discovery of patterns in multigraphs (graphs in which multiple edges can be present between vertices). Our main contributions are twofold. Firstly, we propose a novel subjective

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2020-03-12
Mariam Kiran, Cong Wang, George Papadimitriou, Anirban Mandal, Ewa Deelman

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-04
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik

We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior performance theoretically and empirically. However, most existing algorithms may not be suitable for large-scale datasets because they face repeated computations of a large Gram matrix or require massive hyperparameter optimization

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-23
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

We study the problem of training deep fully connected neural networks with Rectified Linear Unit (ReLU) activation function and cross entropy loss function for binary classification using gradient descent. We show that with proper random weight initialization, gradient descent can find the global minima of the training loss for an over-parameterized deep ReLU network, under certain assumption on the

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-04
Longhao Yuan, Chao Li, Jianting Cao, Qibin Zhao

In recent studies, tensor ring decomposition (TRD) has become a promising model for tensor completion. However, TRD suffers from the rank selection problem due to the undetermined multilinear rank. For tensor decomposition with missing entries, the sub-optimal rank selection of traditional methods leads to the overfitting/underfitting problem. In this paper, we first explore the latent space of the

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-10
Zhi-Hao Tan, Peng Tan, Yuan Jiang, Zhi-Hua Zhou

Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-07
Wenzhang Zhuge, Chenping Hou, Shaoliang Peng, Dongyun Yi

As data can be acquired in an ever-increasing number of ways, multi-view data is becoming more and more available. Considering the high price of labeling data in many machine learning applications, we focus on multi-view semi-supervised classification problem. To address this problem, in this paper, we propose a method called joint consensus and diversity for multi-view semi-supervised classification

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-10
Peng Zhao, Le-Wen Cai, Zhi-Hua Zhou

In many real-world applications, data are often collected in the form of a stream, and thus the distribution usually changes in nature, which is referred to as concept drift in the literature. We propose a novel and effective approach to handle concept drift via model reuse, that is, reusing models trained on previous data to tackle the changes. Each model is associated with a weight representing its

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-07
Qiang Zhou, Yu Chen, Sinno Jialin Pan

This work focuses on distributed optimization for multi-task learning with matrix sparsity regularization. We propose a fast communication-efficient distributed optimization method for solving the problem. With the proposed method, training data of different tasks can be geo-distributed over different local machines, and the tasks can be learned jointly through the matrix sparsity regularization without

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-10
Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan

Considering the data collection and labeling cost in real-world applications, training a model with limited examples is an essential problem in machine learning, visual recognition, etc. Directly training a model on such few-shot learning (FSL) tasks falls into the over-fitting dilemma, which would turn to an effective task-level inductive bias as a key supervision. By treating the few-shot task as

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-10
Nicolas Bougie, Ryutaro Ichise

Reinforcement learning methods rely on rewards provided by the environment that are extrinsic to the agent. However, many real-world scenarios involve sparse or delayed rewards. In such cases, the agent can develop its own intrinsic reward function called curiosity to enable the agent to explore its environment in the quest of new skills. We propose a novel end-to-end curiosity mechanism for deep reinforcement

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2020-02-28
Jaromír Janisch, Tomáš Pevný, Viliam Lisý

This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in its explicit formulation and then convert it into an equivalent MDP, that can be

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2020-02-28
Dan Halbersberg, Maydan Wienreb, Boaz Lerner

Although recent studies have shown that a Bayesian network classifier (BNC) that maximizes the classification accuracy (i.e., minimizes the 0/1 loss function) is a powerful tool in both knowledge representation and classification, this classifier: (1) focuses on the majority class and, therefore, misclassifies minority classes; (2) is usually uninformative about the distribution of misclassifications;

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2020-02-06
Edwin Simpson, Iryna Gurevych

We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples’ opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-15
Jesper E. van Engelen, Holger H. Hoos

Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller sets of labelled data. In recent years, research in this

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-25
Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt

Spreadsheets are arguably the most accessible data-analysis tool and are used by millions of people. Despite the fact that they lie at the core of most business practices, working with spreadsheets can be error prone, usage of formulas requires training and, crucially, spreadsheet users do not have access to state-of-the-art analysis techniques offered by machine learning. To tackle these issues, we

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-25
Jia He, Changying Du, Fuzhen Zhuang, Xin Yin, Qing He, Guoping Long

Multi-view data have become increasingly popular in many real-world applications where data are generated from different information channels or different views such as image + text, audio + video, and webpage + link data. Last decades have witnessed a number of studies devoted to multi-view learning algorithms, especially the predictive latent subspace learning approaches which aim at obtaining a

更新日期：2020-04-22
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-30
Koji Tabata, Atsuyoshi Nakamura, Junya Honda, Tamiki Komatsuzaki

Abstract We study a bad arm existence checking problem in a stochastic K-armed bandit setting, in which a player’s task is to judge whether a positive arm exists or all the arms are negative among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold $$\theta _U$$, and it is negative if that

更新日期：2020-03-02
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-23
Nastasiya F. Grinberg, Oghenejokpeme I. Orhobor, Ross D. King

Abstract In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other

更新日期：2020-03-02
• Mach. Learn. (IF 2.672) Pub Date : 2020-01-24
Dimitris Bertsimas, Bart Van Parys

We present a novel method for sparse polynomial regression. We are interested in that degree r polynomial which depends on at most k inputs, counting at most $$\ell$$ monomial terms, and minimizes the sum of the squares of its prediction errors. Such highly structured sparse regression was denoted by Bach (Advances in neural information processing systems, pp 105–112, 2009) as sparse hierarchical regression

更新日期：2020-01-24
• Mach. Learn. (IF 2.672) Pub Date : 2020-01-23
Emanuele Pesce, Giovanni Montana

Abstract Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for

更新日期：2020-01-23
• Mach. Learn. (IF 2.672) Pub Date : 2019-06-27
Mattia Desana, Christoph Schnörr

This paper introduces a probabilistic architecture called sum–product graphical model (SPGM). SPGMs represent a class of probability distributions that combines, for the first time, the semantics of probabilistic graphical models (GMs) with the evaluation efficiency of sum–product networks (SPNs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific

更新日期：2020-01-17
• Mach. Learn. (IF 2.672) Pub Date : 2019-07-25
Vojtěch Kovařík, Viliam Lisý

Abstract Hannan consistency, or no external regret, is a key concept for learning in games. An action selection algorithm is Hannan consistent (HC) if its performance is eventually as good as selecting the best fixed action in hindsight. If both players in a zero-sum normal form game use a Hannan consistent algorithm, their average behavior converges to a Nash equilibrium of the game. A similar result

更新日期：2020-01-17
• Mach. Learn. (IF 2.672) Pub Date : 2019-06-26
Huan Li, Zhouchen Lin

Optimization over low rank matrices has broad applications in machine learning. For large-scale problems, an attractive heuristic is to factorize the low rank matrix to a product of two much smaller matrices. In this paper, we study the nonconvex problem $$\min _{\mathbf {U}\in \mathbb {R}^{n\times r}} g(\mathbf {U})=f(\mathbf {U}\mathbf {U}^T)$$ under the assumptions that $$f(\mathbf {X})$$ is restricted

更新日期：2020-01-17
• Mach. Learn. (IF 2.672) Pub Date : 2019-08-12
Harold Connamacher, Nikil Pancha, Rui Liu, Soumya Ray

Abstract Rankboost is a well-known algorithm that iteratively creates and aggregates a collection of “weak rankers” to build an effective ranking procedure. Initial work on Rankboost proposed two variants. One variant, that we call Rb-d and which is designed for the scenario where all weak rankers have the binary range $$\{0,1\}$$, has good theoretical properties, but does not perform well in practice

更新日期：2020-01-17
• Mach. Learn. (IF 2.672) Pub Date : 2019-08-22
Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt

Abstract Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the function. In this work, we explore strategies to combine these techniques for better global optimization. In particular, we propose ways to use the Lipschitz continuity assumption within traditional BO algorithms, which

更新日期：2020-01-17
• Mach. Learn. (IF 2.672) Pub Date : 2019-10-02
Alberto Cano, Bartosz Krawczyk

Learning from data streams in the presence of concept drift is among the biggest challenges of contemporary machine learning. Algorithms designed for such scenarios must take into an account the potentially unbounded size of data, its constantly changing nature, and the requirement for real-time processing. Ensemble approaches for data stream mining have gained significant popularity, due to their

更新日期：2020-01-17
• Mach. Learn. (IF 2.672) Pub Date : 2020-01-13
Esteban G. Tabak, Giulio Trigila, Wenjun Zhao

A methodology to estimate from samples the probability density of a random variable x conditional to the values of a set of covariates $$\{z_{l}\}$$ is proposed. The methodology relies on a data-driven formulation of the Wasserstein barycenter, posed as a minimax problem in terms of the conditional map carrying each sample point to the barycenter and a potential characterizing the inverse of this map

更新日期：2020-01-13
• Mach. Learn. (IF 2.672) Pub Date : 2020-01-07
Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin

Learning from large-scale and high-dimensional data still remains a computationally challenging problem, though it has received increasing interest recently. To address this issue, randomized reduction methods have been developed by either reducing the dimensionality or reducing the number of training instances to obtain a small sketch of the original data. In this paper, we focus on recovering a high-dimensional

更新日期：2020-01-07
• Mach. Learn. (IF 2.672) Pub Date : 2019-12-03
Andrew Cropper, Rolf Morel, Stephen Muggleton

Abstract A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher-order programs by allowing for higher-order definitions to be used as background

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-06-04
Kshitij Khare, Sang-Yun Oh, Syed Rahman, Bala Rajaratnam

Abstract Covariance estimation for high-dimensional datasets is a fundamental problem in machine learning, and has numerous applications. In these high-dimensional settings the number of features or variables p is typically larger than the sample size n. A popular way of tackling this challenge is to induce sparsity in the covariance matrix, its inverse or a relevant transformation. In many applications

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-06-26
Qidi Peng, Nan Rao, Ran Zhao

We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a formal criterion on the efficiency of dissimilarity measures, and discuss an approach to improve the efficiency

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-05-23
Di Ma, Songcan Chen

Support matrix machine (SMM) is an efficient matrix classification method that can leverage the structure information within the matrix to improve the classification performance. However, its computational and storage costs are still expensive for high-dimensional data. To address these problems, in this paper, we consider a 2D compressed learning paradigm to learn the SMM classifier in some compressed

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-06-18
Gregor H. W. Gebhardt, Andras Kupcsik, Gerhard Neumann

Abstract Enabling robots to act in unstructured and unknown environments requires versatile state estimation techniques. While traditional state estimation methods require known models and make strong assumptions about the dynamics, such versatile techniques should be able to deal with high dimensional observations and non-linear, unknown system dynamics. The recent framework for nonparametric inference

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-06-18
Eric Bax, Lingjie Weng, Xu Tian

Abstract We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that k-nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with $$\hbox {O} \left( \sqrt{(k + \ln n)/n} \right)$$ range around an estimate of generalization error for n in-sample examples.

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-20
Andrew Cropper, Sophie Tourret

Many forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-18
Andrew Cropper, Richard Evans, Mark Law

General game playing (GGP) is a framework for evaluating an agent’s general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game (described as a logic program) that it has never seen before. The task is for the agent to play the game, thus generating game traces. The winner of the GGP competition is the agent that gets the best total score over all

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-13
Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff

In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our interest is in problems that have the following characteristics: (a) optimal, or even near-optimal solutions are very rare; (b) it is expensive to obtain the value of the objective function for large numbers of data instances; and (c) there is domain knowledge

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-13
Soham Sarkar, Rahul Biswas, Anil K. Ghosh

Abstract Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, which can be used for data of arbitrary dimensions. Most of these test statistics are computed using pairwise Euclidean distances among

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-11-08
Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama

Reinforcement learning (RL) is a machine learning technique aiming to learn how to take actions in an environment to maximize some kind of reward. Recent research has shown that although the learning efficiency of RL can be improved with expert demonstration, it usually takes considerable efforts to obtain enough demonstration. The efforts prevent training decent RL agents with expert demonstration

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2019-05-16
Alex Luedtke, Emilie Kaufmann, Antoine Chambaz

Abstract We study a generalization of the multi-armed bandit problem with multiple plays where there is a cost associated with pulling each arm and the agent has a budget at each time that dictates how much she can expect to spend. We derive an asymptotic regret lower bound for any uniformly efficient algorithm in our setting. We then study a variant of Thompson sampling for Bernoulli rewards and a

更新日期：2020-01-04
• Mach. Learn. (IF 2.672) Pub Date : 2020-01-02
Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu

Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the

更新日期：2020-01-02
• Mach. Learn. (IF 2.672) Pub Date : 2019-12-26
Alberto N. Escalante-B., Laurenz Wiskott

Slow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a multi-dimensional time series. SFA has been extended to supervised learning (classification and regression) by an algorithm called graph-based SFA (GSFA). GSFA relies on a particular graph structure to extract features that preserve label similarities. Processing of high dimensional input

更新日期：2019-12-26
• Mach. Learn. (IF 2.672) Pub Date : 2019-12-24
Johannes Fürnkranz, Tomáš Kliegr, Heiko Paulheim

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly

更新日期：2019-12-24
• Mach. Learn. (IF 2.672) Pub Date : 2019-12-18
Ching-pei Lee, Kai-Wei Chang

In recent years, there is a growing need to train machine learning models on a huge volume of data. Therefore, designing efficient distributed optimization algorithms for empirical risk minimization (ERM) has become an active and challenging research topic. In this paper, we propose a flexible framework for distributed ERM training through solving the dual problem, which provides a unified description

更新日期：2019-12-18
• Mach. Learn. (IF 2.672) Pub Date : 2019-12-03
Gianvito Pio, Michelangelo Ceci, Francesca Prisciandaro, Donato Malerba

Gene network reconstruction is a bioinformatics task that aims at modelling the complex regulatory activities that may occur among genes. This task is typically solved by means of link prediction methods that analyze gene expression data. However, the reconstructed networks often suffer from a high amount of false positive edges, which are actually the result of indirect regulation activities due to

更新日期：2019-12-03
• Mach. Learn. (IF 2.672) Pub Date : 2019-03-25
Ioannis Tsamardinos,Giorgos Borboudakis,Pavlos Katsogridakis,Polyvios Pratikakis,Vassilis Christophides

We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) for Big Data of high dimensionality. PFBP partitions the data matrix both in terms of rows as well as columns. By employing the concepts of p-values of conditional independence tests and meta-analysis techniques, PFBP relies only on computations local to a partition while minimizing communication costs

更新日期：2019-11-01
• Mach. Learn. (IF 2.672) Pub Date : 2018-11-06
Ioannis Tsamardinos,Elissavet Greasidou,Giorgos Borboudakis

Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of hyper-parameters (called a configuration) for producing the final predictive model, and (b) estimating the predictive performance of the final model. However, the cross-validated performance of the best configuration is optimistically

更新日期：2019-11-01
• Mach. Learn. (IF 2.672) Pub Date : 2017-10-01
NhatHai Phan,Xintao Wu,Dejing Dou

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private

更新日期：2019-11-01
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