support vector machine
PulseAugur coverage of support vector machine — every cluster mentioning support vector machine across labs, papers, and developer communities, ranked by signal.
- instance of naive Bayes classifier 90%
- instance of k-nearest neighbors algorithm 90%
- instance of logistic regression model 70%
- competes with logistic regression model 70%
- uses BiLSTM 70%
- competes with naive Bayes classifier 70%
- instance of decision tree 70%
- instance of LightGBM 70%
- used by lasso 70%
- competes with multilayer perceptron 70%
- used by BiLSTM 70%
- used by decision tree 70%
14 day(s) with sentiment data
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Machine learning models detect user deaths on social media
A new dissertation details the development of machine learning classifiers capable of automatically detecting deceased users on social networking sites. The research utilized a new dataset compiled from Wikidata and X (…
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AI maps lung cancer growth patterns using visual vocabulary
Researchers have developed a novel weakly supervised Bag-of-Visual-Words (BoVW) pipeline to map lung adenocarcinoma growth patterns from whole slide images. This method utilizes frozen foundation model embeddings to lea…
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Machine learning framework enhances QKD security against stealthy attacks
Researchers have developed a novel machine learning framework to enhance the detection of eavesdropping attacks in BB84 Quantum Key Distribution (QKD) systems. This framework moves beyond the traditional fixed QBER thre…
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New q-Orthogonal Kernels Enhance SVM Performance and Stability
Researchers have introduced a new family of kernels for machine learning, specifically for Support Vector Machines (SVMs), based on discrete $q$-Hermite I polynomials. These $q$-orthogonal kernels generalize classical H…
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Satellite imagery reveals significant vegetation loss in Indonesia's new capital city
A new study published on arXiv details the environmental impact of Indonesia's capital relocation to Ibu Kota Nusantara (IKN). Using PlanetScope satellite imagery and remote sensing techniques, researchers tracked land …
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New SingLEM model offers reusable EEG representations from single channels
Researchers have developed SingLEM, a self-supervised foundation model designed for electroencephalography (EEG) data. This model addresses the limitations of current task-specific EEG models by creating reusable repres…
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New SVM method learns optimal pinball-loss parameters dynamically
Researchers have developed a novel data-driven approach for Elastic-Net Support Vector Machines (SVMs) that dynamically selects optimal pinball-loss parameters. This method learns simplex-constrained weights over candid…
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New POSSE-kNN method improves binary classification accuracy
A new machine learning method called POSSE-kNN has been developed for binary classification tasks, particularly for tabular data. This ensemble technique combines bootstrap sampling, random feature subspaces, out-of-bag…
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Machine learning pilot study shows promise for COVID-19 classification from X-rays
Researchers have conducted a pilot study using traditional machine learning techniques to classify COVID-19 from other pneumonias using chest X-ray data. By employing texture and gradient-based features with classifiers…
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XGBoost outperforms Transformer models in Ethereum Sybil bot detection
A new research paper published on arXiv evaluates different machine learning models for detecting Sybil bots on the Ethereum blockchain. The study introduces a leakage-aware evaluation framework and a "Transaction Gramm…
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Classical ML models show power-law scaling on tabular data
A new study published on arXiv benchmarks classical machine learning models on tabular data, revealing that power laws accurately describe learning curves across various datasets and model families. The research found t…
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AI system fuses CNN and GLCM features for 95% accurate cataract grading
Researchers have developed a novel system for classifying cataract severity using a fusion of deep learning and traditional image processing techniques. This hybrid approach combines features from a Convolutional Neural…
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New framework auto-tunes SVMs on quantum annealers
This paper introduces a novel framework for optimizing Support Vector Machines (SVMs) that utilize Quadratic Unconstrained Binary Optimization (QUBO) models on quantum-inspired annealers. The framework employs Optuna fo…
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New geometric approach optimizes SVM hyperplanes iteratively
Researchers have developed a novel iterative geometric approach to optimize separating hyperplanes, specifically for the hard-margin Support Vector Machine (SVM) classifier. This method aims to improve the efficiency of…
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Machine learning models benchmarked for breast cancer prediction using multi-omics data
Researchers have benchmarked several machine learning models for predicting Estrogen Receptor (ER) status in breast cancer using multi-omics data. The study found that RNA expression data provided the strongest predicti…
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New concordance loss improves deep survival prediction models
Researchers have developed a new concordance loss function, called SCL (Sigmoid Concordance Loss), for deep survival prediction models. Traditional models often use likelihood objectives that do not reliably correlate w…
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Study compares CNNs and handcrafted features for facial expression recognition
A new study empirically compares handcrafted feature learning methods like Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) against Convolutional Neural Networks (CNNs) for facial expression recogni…
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Single-channel sEMG shows promise for efficient hand gesture recognition
Researchers have explored the use of a single surface electromyography (sEMG) channel for hand gesture classification, aiming for more efficient and low-power systems. By extracting various time-domain and frequency-dom…
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Urdu fake news detection hindered by dataset length confound
Researchers have conducted a study on Urdu fake news detection, highlighting significant challenges in cross-dataset generalization. Using the XLM-RoBERTa model and two distinct Urdu datasets, the study found that while…
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Classical ML methods show promise in detecting LLM-generated text
Researchers are exploring the use of traditional machine learning models to detect text generated by large language models (LLMs). These classical methods, such as Support Vector Machines and Naive Bayes classifiers, of…