The Weighted Majority Algorithm

Littlestone, Nick; Warmuth, Manfred K.

doi:10.1006/inco.1994.1009

Cited by 1,347 publications

(446 citation statements)

References 1 publication

Supporting

Mentioning

422

Contrasting

Unclassified

Order By: Relevance

“…We use the tightest available PAC-learning bounds, due to Anthony, Biggs and Shawe-Taylor (1990), to argue that with high probability, a hypothesis consistent with the subsample can't be too bad on the whole sample. Littlestone and Warmuth (1989) describe a variant of the weighted majority Mgorithm where the weights are kept above some lower limit. This allows the weighted majority algorithm to recover and adapt to changes in the target.…”

Section: Introductionmentioning

confidence: 99%

Tracking drifting concepts by minimizing disagreements

Helmbold

Long

1994

Mach Learn

View full text Add to dashboard Cite

Editors: Ming Li and Leslie ValiantA b s t r a c t . In this paper we consider the problem of tracking a subset of a domain (called the target) which changes gradually over time. A single (unknown) probability distribution over the domain is used to generate random examples for the learning algorithm and measure the speed at which the target changes. Clearly, the more rapidly the target moves, the harder it is for the algorithm to maintain a good approximation of the target. Therefore we evaluate algorithms based on how much movement of the target can be tolerated between examples while predicting with accuracy e. Furthermore, the complexity of the class 7-/of possible targets, as measured by d, its VC-dimension, also effects the difficulty of tracking the target concept. We show that if the problem of minimizing the number of disagreements with a sample from among concepts in a class 7{ can be approximated to within a factor k, then there is a simple tracking algorithm for 7-t which can achieve a probability e of making a mistake if the target movement rate is at most a constant times e2/(k(d + k) In 1), where d is the Vapnik-Chervonenkis dimension of 7-t. Also, we show that if 7-/ is properly PAC-learnable, then there is an efficient (randomized) algorithm that with high probability approximately minimizes disagreements to within a factor of 7d + 1, yielding an efficient tracking algorithm for 7-I which tolerates drift rates up to a constant times e2/(d 2 In ¼). In addition, we prove complementary results for the classes of halfspaces and axisaligned hyperrectangles showing that the maximum rate of drift that any algorithm (even with unlimited computational power) can tolerate is a constant times e2/d.

show abstract

Section: Introductionmentioning

confidence: 99%

Tracking drifting concepts by minimizing disagreements

Helmbold

Long

1994

Mach Learn

View full text Add to dashboard Cite

show abstract

“…The training error ε i indicates the fitting degree of Classifier C i to the training data. This estimation has been validated in [62]. For easier interpretation, we can normalize these weights so that they sum up to 1; however, normalization does not change the outcome of weighted majority voting.…”

Section: Weighted-majority Votingmentioning

confidence: 93%

“…Weighted-majority voting (WMV) is a meta-learning reasoning procedure [62]. The first type of majority voting refers to the decision when all experts agree on the same output (unanimous voting).…”

Section: Weighted-majority Votingmentioning

confidence: 99%

Knowledge-Driven Board-Level Functional Fault Diagnosis

Zhang

Chakrabarty

et al. 2017

View full text Add to dashboard Cite

The semiconductor industry continues to relentlessly advance silicon technology scaling into the deep-submicron (DSM) era. High integration levels and structured design methods enable complex systems that can be manufactured in high volume.However, due to increasing integration densities and high operating speeds, subtle manifestation of defects leads to functional failures at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results can lead to long debug times and wrong repair actions, which significantly increase repair cost and adversely impact yield.

show abstract

“…Pool of models for the ensemble method! Figure 1 presents the details of the proposed ensemble method, which implements a modified version of the weighted majority algorithm (WMA) [11]. The modified WMA returns a KPI degradation level in the range [0,1] and uses context information for updating the weights and creating new models.…”

Section: M3!mentioning

confidence: 99%

Detecting anomalies in cellular networks using an ensemble method

Ciocarlie

Lindqvist

Nováczki³

et al. 2013

Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013)

View full text Add to dashboard Cite

Abstract-The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. This paper focuses on the problem of cell anomaly detection, addressing partial and complete degradations in cell-service performance, and it proposes an adaptive ensemble method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to determine cell-performance status and is able to cope with legitimate system changes (i.e., concept drift). The results, generated using real cellular network data, suggest that the proposed ensemble method automatically and significantly improves the detection quality over univariate and multivariate methods, while using intrinsic system knowledge to enhance performance.

show abstract

The Weighted Majority Algorithm

Cited by 1,347 publications

References 1 publication

Tracking drifting concepts by minimizing disagreements

Tracking drifting concepts by minimizing disagreements

Knowledge-Driven Board-Level Functional Fault Diagnosis

Detecting anomalies in cellular networks using an ensemble method

Contact Info

Product

Resources

About