Keyphrases
Metric Space
100%
Sample Complexity
93%
Active Learning
81%
Approximation Factor
69%
Constant-factor Approximation Algorithm
60%
Label Complexity
58%
Learning Settings
53%
Binary Classification
52%
Multiple Instance Learning
51%
Nearest Neighbor
51%
Feature Feedback
51%
K-median Clustering
51%
Logit
51%
Discriminative Features
51%
K-median
51%
Learning Algorithm
50%
Approximation Guarantee
45%
Half-space
42%
Interactive Algorithm
42%
Iterative Procedure
42%
Oracle
42%
Bayes
42%
Low Error
40%
K-means
39%
Bayes Consistency
39%
Interactive Learning
37%
One-class Learning
34%
Tight
34%
Linear Regression
34%
Large Margin Learning
34%
Greedy Algorithm
34%
Active Feature Selection
34%
Information Bottleneck
34%
Active Structure
34%
One-class Classification
34%
Structure Learning
34%
Bayesian Network
34%
System Log
34%
Round numbers
34%
Target Distribution
34%
Categorical Features
34%
Generalization Properties
34%
Pool-based
32%
Generalization Error
30%
Mutual Information
30%
Machine Learning
29%
Stream-based
28%
Active Learner
28%
Multi-arm Bandit
27%
Pure Exploration
27%
Computer Science
Active Learning
100%
Learning Algorithm
90%
Approximation (Algorithm)
85%
approximation factor
54%
Instance Learning
51%
Binary Classification
51%
Discriminative Feature
51%
Classification Task
51%
Metric Space
51%
Supervised Learning
44%
Feature Selection
42%
Computer System
42%
Computational Capacity
34%
Running Machine
34%
Random Variable
34%
Bayesian Networks
34%
Target Distribution
34%
Generalization Error
34%
structure learning
34%
Underlying Distribution
34%
Mean Algorithm
34%
k-means Clustering
34%
Compression Scheme
34%
Class Classification
34%
Mutual Information
30%
Multiple Instance
28%
Classification Accuracy
27%
Support Vector Machine
25%
Complexity Reduction
25%
Resulting Cluster
25%
Presented Example
25%
Target Population
25%
Search Engine
25%
Total Variation
25%
Machine Learning
24%
Unlabeled Data
23%
Real Data Sets
22%
Information Criterion
20%
Based Active Learning
17%
Clustering Method
17%
Algorithm Selection
17%
Behavior Analysis
17%
Enabling System
17%
Learning Approach
17%
Data Source
17%
Relative Error
17%
Separating Hyperplane
17%
System Behavior
17%
Positive Example
17%
k-Nearest Neighbors Algorithm
17%
Mathematics
Metric Space
91%
Binary Classification
85%
Nearest Neighbor
57%
Median
51%
Linear Regression
51%
Lower and upper bounds
42%
Stochastics
42%
Neural Network
38%
Cluster Center
34%
Input Data
34%
Greedy Algorithm
34%
Loss Function
34%
Underlying Distribution
34%
Random Variable
34%
Gaussian Distribution
34%
Probability Theory
34%
Concludes
31%
Heavy Tail
25%
Regularization
25%
Random Sample
25%
Simple Function
25%
Covariate
25%
Approximates
25%
Cost Function
25%
Worst Case
25%
Parametric
24%
Training Set
20%
Empirical Risk Minimization
17%
Heuristic Algorithm
17%
Sharp Contrast
17%
Permutation
17%
Approximation Error
17%
Decision Tree
17%
Covariance Matrix
17%
Combinatorial Structure
17%
Random Order
17%
Dependent Sample
17%
Regression Model
17%
Bayesian Network
17%
Robust Version
17%
Fraud Detection
17%
Robust Algorithm
17%
Stochastic Search
17%
Minimal Sufficient Statistic
17%
Training Data
17%
Convergence Analysis
17%
Fast Algorithm
17%
Misspecified Model
17%
structure learning
17%
Constant Factor
17%
