Pages that link to "Item:Q3577032"

The following pages link to Learning to Rank for Information Retrieval (Q3577032):

Displaying 35 items.

• Learning multiple metrics for ranking (Q352075) (← links)
• Probabilistic generative ranking method based on multi-support vector domain description (Q497582) (← links)
• A feature-centric view of information retrieval. (Q639421) (← links)
• DEARank: a data-envelopment-analysis-based ranking method (Q890309) (← links)
• Learning to rank: a ROC-based graph-theoretic approach (Q976981) (← links)
• Tolerant information retrieval with backpropagation networks (Q1606658) (← links)
• Utilizing sources of evidence in relevance feedback through geometry (Q1628566) (← links)
• Dyad ranking using Plackett-Luce models based on joint feature representations (Q1640577) (← links)
• Learning to rank in PRISM (Q1726418) (← links)
• Exploiting explicit and implicit feedback for personalized ranking (Q1792853) (← links)
• A review on instance ranking problems in statistical learning (Q2127240) (← links)
• Generalized vec trick for fast learning of pairwise kernel models (Q2127249) (← links)
• Optimal full ranking from pairwise comparisons (Q2148998) (← links)
• How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (Q2240005) (← links)
• Editorial: Preference learning and ranking (Q2251428) (← links)
• Robust ordinal regression in preference learning and ranking (Q2251445) (← links)
• Generalized transitivity: a systematic comparison of concepts with an application to preferences in the Babington Smith model (Q2300468) (← links)
• Adjacency-based regularization for partially ranked data with non-ignorable missing (Q2305301) (← links)
• Semi-parametric order-based generalized multivariate regression (Q2400819) (← links)
• \texttt{Procrustes}: a python library to find transformations that maximize the similarity between matrices (Q2695590) (← links)
• Row and Column Generation Algorithm for Maximization of Minimum Margin for Ranking Problems (Q2806938) (← links)
• Learning linear ranking functions for beam search with application to planning (Q2880942) (← links)
• QoRank: A query-dependent ranking model using LSE-based weighted multiple hyperplanes aggregation for information retrieval (Q3072522) (← links)
• Active Learning with Multiple Localized Regression Models (Q4599320) (← links)
• Research advances and prospects of learning to rank (Q4623550) (← links)
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• <i>U</i>-Processes and Preference Learning (Q5383812) (← links)
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• Large Scale Ranking Using Stochastic Gradient Descent (Q5881562) (← links)
• Listwise approaches based on feature ranking discovery (Q5964249) (← links)
• \textsc{PeerNomination}: a novel peer selection algorithm to handle strategic and noisy assessments (Q6098845) (← links)
• Subgraph nomination: query by example subgraph retrieval in networks (Q6171795) (← links)
• Graph neural networks-based preference learning method for object ranking (Q6548454) (← links)
• Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies. II (Q6614639) (← links)