Browsing by Author "Gavaldà Mestre, Ricard"
Now showing items 1-20 of 90
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A case study of improving a non-technical losses detection system through explainability
(Springer Nature, 2023-01-01)
Article
Open AccessDetecting and reacting to non-technical losses (NTL) is a fundamental activity that energy providers need to face in their daily routines. This is known to be challenging since the phenomenon of NTL is multi-factored, ... -
A lower bound for learning distributions generated by probabilistic automata
(Springer, 2010)
Conference report
Open AccessKnown algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. ... -
A new spectral method for latent variable models
(2018-05-22)
Article
Open AccessWe present an algorithm for the unsupervised learning of latent variable models based on the method of moments. We give efficient estimates of the moments for two models that are well known, e.g., in text mining, the ... -
A note on the query complexity of learning DFA
(1992)
Research report
Open AccessIt is known that the class of deterministic finite automata is polynomial time learnable by using membership and equivalence queries. We investigate -- the query complexity -- the number of membership and equivalence queries ... -
A positive relativization of polynomial time vs. polylog space
(1991-05)
Research report
Open AccessCan every set in P be solved in polylogarithmic space? We show that this question is equivalent to asking whether the classes PSPACE and EXPTIME are always equal under relativization. We use an oracle access mechanism that ... -
Adaptive distributed mechanism againts flooding network attacks based on machine learning
(ACM Press, NY, 2008)
Conference report
Restricted access - publisher's policyAdaptive techniques based on machine learning and data mining are gaining relevance in self-management and self- defense for networks and distributed systems. In this paper, we focus on early detection and stopping of ... -
Adaptive on-line software aging prediction based on machine learning
(IEEE Computer Society Publications, 2010)
Conference report
Open AccessThe growing complexity of software systems is resulting in an increasing number of software faults. According to the literature, software faults are becoming one of the main sources of unplanned system outages, and have ... -
Adaptive parameter-free learning from evolving data streams
(2009-03)
Research report
Open AccessWe propose and illustrate a method for developing algorithms that can adaptively learn from data streams that change over time. As an example, we take Hoeffding Tree, an incremental decision tree inducer for data streams, ... -
Adaptive sampling methods for scaling up knowledge discovery algorithms
(2001-07)
Research report
Open AccessOne of the biggest research challenges in KDD and Data Mining is to develop methods that scale up well to large amounts of data. A possible approach for achieving scalability is to take a random sample and do data mining ... -
Adaptive scheduling on power-aware managed data-centers using machine learning
(IEEE Computer Society Publications, 2011)
Conference report
Restricted access - publisher's policyEnergy-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power-aware resource management strategies, also known ... -
Adaptive scheduling on power-aware managed data-centers using machine learning
(2011)
Research report
Open AccessEnergy-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power-aware resource management strategies, also known ... -
Adaptively learning probabilistic deterministic automata from data streams
(2014-07)
Article
Restricted access - publisher's policyMarkovian models with hidden state are widely-used formalisms for modeling sequential phenomena. Learnability of these models has been well studied when the sample is given in batch mode, and algorithms with PAC-like ... -
Adarules: Learning rules for real-time road-traffic prediction
(Elsevier, 2017-12-17)
Conference report
Open AccessTraffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key ... -
Algorithms for learning finite automata from queries: a unified view
(1996-09)
Research report
Open AccessIn this survey we compare several known variants of the algorithm for learning deterministic finite automata via membership and equivalence queries. We believe that our presentation makes it easier to understand what ... -
An approach to correctness of data parallel algorithms
(1991-05)
Research report
Open AccessThe design of data parallel algorithms for fine-grained SIMD machines is a fundamental domain in today computer science. High standards in the specification and resolution of problems have been achieved in the sequential ... -
An efficient closed frequent itemset miner for the MOA stream mining system
(2015-01-07)
Article
Open AccessMining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent ... -
An efficient closed frequent itemset miner for the MOA stream mining system
(2013)
Research report
Open AccessMining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent ... -
An integer linear programming representation for data-center power-aware management
(2010-11-12)
Research report
Open AccessThis work exposes how to represent a grid data-center based scheduling problem, taking the advantages of the virtualization and consolidation techniques, as a linear integer programming problem including all three mentioned ... -
An optimal anytime estimation algorithm
(2004-12)
Research report
Open AccessIn many applications a key step is estimating some unknown quantity ~$mu$ from a sequence of trials, each having expected value~$mu$. Optimal algorithms are known when the task is to estimate $mu$ within a multiplicative ... -
An optimal parallel algorithm for learning DFA
(Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics, 1993-01-01)
Research report
Open AccessIn 1987, D. Angluin presented an algorithm that exactly learns regular languages represented by deterministic finite automata (dfa) from Membership and Equivalence queries. Furthermore, the algorithm is feasible in the ...
