Keyphrases
Sample Complexity
100%
Metric Space
78%
Active Learning
77%
Label Complexity
55%
Approximation Factor
54%
Multiple Instance Learning
53%
Multi-class
47%
Half-space
47%
Constant-factor Approximation Algorithm
47%
Binary Classification
46%
Learning Settings
44%
Low Error
40%
Nearest Neighbor
40%
Feature Feedback
40%
K-median Clustering
40%
Logit
40%
Learnability
40%
Discriminative Features
40%
K-median
40%
Approximation Guarantee
40%
Learning Algorithm
39%
Empirical Risk Minimization
38%
Interactive Algorithm
33%
Iterative Procedure
33%
Oracle
33%
Bayes
33%
One-class Learning
32%
K-means
31%
Bayes Consistency
31%
Interactive Learning
29%
Tight
27%
Linear Regression
26%
Arbitrary Dependence
26%
Large Margin Learning
26%
Greedy Algorithm
26%
Active Feature Selection
26%
Information Bottleneck
26%
Active Structure
26%
One-class Classification
26%
Structure Learning
26%
Bayesian Network
26%
System Log
26%
Round numbers
26%
Target Distribution
26%
Categorical Features
26%
Generalization Properties
26%
Active Learner
26%
Pool-based
25%
Generalization Error
24%
Mutual Information
23%
Computer Science
Active Learning
92%
Approximation (Algorithm)
74%
Learning Algorithm
71%
Instance Learning
53%
Binary Classification
44%
approximation factor
42%
Discriminative Feature
40%
Classification Task
40%
Metric Space
40%
Supervised Learning
37%
Multiple Instance
34%
Feature Selection
33%
Computer System
33%
Computational Capacity
26%
Running Machine
26%
Random Variable
26%
Bayesian Networks
26%
Target Distribution
26%
Generalization Error
26%
structure learning
26%
Underlying Distribution
26%
Mean Algorithm
26%
k-means Clustering
26%
Compression Scheme
26%
Class Classification
26%
Risk Minimization
26%
Mutual Information
24%
Classification Accuracy
21%
Support Vector Machine
20%
Based Active Learning
20%
Complexity Reduction
20%
Resulting Cluster
20%
Presented Example
20%
Target Population
20%
Search Engine
20%
Total Variation
20%
Machine Learning
19%
Unlabeled Data
18%
Real Data Sets
17%
Information Criterion
16%
Clustering Method
13%
Algorithm Selection
13%
Behavior Analysis
13%
Enabling System
13%
Learning Approach
13%
Data Source
13%
Relative Error
13%
Separating Hyperplane
13%
System Behavior
13%
Positive Example
13%
Mathematics
Binary Classification
71%
Metric Space
71%
Nearest Neighbor
45%
Empirical Risk Minimization
40%
Median
40%
Linear Regression
40%
Lower and upper bounds
38%
Stochastics
33%
Neural Network
30%
Cluster Center
26%
Input Data
26%
Greedy Algorithm
26%
Heuristic Algorithm
26%
Loss Function
26%
Underlying Distribution
26%
Random Variable
26%
Gaussian Distribution
26%
Probability Theory
26%
Concludes
24%
Heavy Tail
20%
Regularization
20%
Random Sample
20%
Simple Function
20%
Covariate
20%
Approximates
20%
Cost Function
20%
Parametric
19%
Sharp Contrast
17%
Permutation
17%
Training Set
15%
Approximation Error
13%
Decision Tree
13%
Covariance Matrix
13%
Combinatorial Structure
13%
Random Order
13%
Dependent Sample
13%
Regression Model
13%
Bayesian Network
13%
Robust Version
13%
Fraud Detection
13%
Robust Algorithm
13%
Stochastic Search
13%
Minimal Sufficient Statistic
13%
Training Data
13%
Convergence Analysis
13%
Fast Algorithm
13%
Misspecified Model
13%
structure learning
13%
Constant Factor
13%
Nonuniform
13%
