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Record W2348148951

Underwater Target Fusion Recognition Based on Non-linear Dimensionality Reduction

2008· article· en· W2348148951 on OpenAlexvenueno aff
Yue Zhou

Bibliographic record

VenueMicrocomputer applications · 2008
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsnot available
Fundersnot available
KeywordsDimensionality reductionComputer scienceArtificial intelligencePattern recognition (psychology)UnderwaterClassifier (UML)Support vector machineComputationCurse of dimensionalityFeature vectorAlgorithm
DOInot available

Abstract

fetched live from OpenAlex

Aiming at the difficulty of handling high dimension data of underwater target feature extracted by tradition methods of spectrum and model parameters,this paper presents a novel underwater targets recognition method by introducing non-linear reduction method,which can effectively eliminate the correlation between vectors of eigenvector,and therefore decrease the computation complexity of post-phase recognition. Additionally,we designed a fusion decision classifier called DS-SVM classifier to classify feature vectors gained from non-linear dimensionality reduction.The experimental results illustrated the superior performance of the proposed method compared with traditional SVM classifier.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.245
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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