Overview of Machine Learning

1
Overview of Machine Learning

• Raymond J. Mooney
• Department of Computer Sciences
• University of Texas at Austin

2
What is Learning?

• Definition by H. Simon Any process by which a
  system improves performance.
• What is the task?
• Classification/categorization
• Problem solving
• Planning
• Control
• Language understanding

3
Classification Examples

• Medical diagnosis
• Credit card applications or transactions
• DNA sequences
• Promoter
• Splice-junction
• Protein structure
• Spoken words
• Handwritten characters
• Astronomical images
• Market basket analysis

4
Other Tasks

• Solving calculus problems
• Playing games
• Checkers
• Chess
• Backgammon
• Pole balancing
• Driving a car
• Flying a helicopter
• Robot navigation

5
How is Performance Measured?

• Classification accuracy
• False positives
• False negatives
• Precision/Recall/F-measure
• Solution correctness and quality (optimality)
• Number of questions answered correctly
• Distance traveled for navigation problem
• Percentage of games won against an opponent
• Time to find a solution

6
Training Experience

• Direct supervision
• Checkers board positions labeled with correct
  move.
• Road images with correct steering position.
• Indirect supervision (delayed reward,
  reinforcement learning)
• Choose sequence of checkers move and eventually
  win or lose game.
• Drive car and rewarded if reach destination.

7
Types of Direct Supervision

• Examples chosen by a benevolent teacher
• Near miss negative examples
• Random examples from the environment.
• Positive and negative examples
• Positive examples only
• Choose examples for a teacher (oracle) to
  classify.
• Design and run ones own experiments.

8
Categorization

• Given
• A description of an instance, x?X, where X is the
  instance language or instance space.
• A fixed set of categories
  Cc1, c2,cn
• A categorization function, c(x), whose domain is
  X and whose range is C.
• Determine
• The category of x c(x)?C,

9
Learning for Categorization

• A training example is an instance x?X, paired
  with its correct category c(x) ltx, c(x)gt for an
  unknown categorization function, c.
• Given
• A set of training examples, D.
• An hypothesis space, H, of possible
  categorization functions, h(x).
• Find a consistent hypothesis, h(x)?H, such that

10
Sample Category Learning Problem

• Instance language ltsize, color, shapegt
• size ? small, medium, large
• color ? red, blue, green
• shape ? square, circle, triangle
• C positive, negative
• D

11
General Learning Issues

• Many hypotheses are usually consistent with the
  training data.
• Bias
• Any criteria other than consistency with the
  training data that is used to select a
  hypothesis.
• Classification accuracy ( of instances
  classified correctly).
• Measured on independent test data.
• Training time (efficiency of training algorithm).
• Testing time (efficiency of subsequent
  classification).

12
Learning as Search

• Learning for categorization requires searching
  for a consistent hypothesis in a given space, H.
• Enumerate and test is a possible algorithm for
  any finite or countably infinite H.
• Most hypothesis spaces are very large
• Conjunctions on n binary features 3n
• All binary functions on n binary features 2
• Efficient algorithms needed for finding a
  consistent hypothesis without enumerating them
  all.

2n
13
Types of Bias

• Language Bias Limit hypothesis space a priori to
  a restricted set of functions.
• Search Bias Employ a hypothesis space that
  includes all possible functions but use a search
  algorithm that prefers simpler hypotheses.
• Since finding the simplest hypothesis is usually
  intractable (e.g. NP-Hard), satisficing heuristic
  search is usually employed.

14
Generalization

• Hypotheses must generalize to correctly classify
  instances not in the training data.
• Simply memorizing training examples is a
  consistent hypothesis that does not generalize.
• Occams razor
• Finding a simple hypothesis helps ensure
  generalization.

15
Over-Fitting

• Frequently, complete consistency with the
  training data is not desirable.
• A completely consistent hypothesis may be fitting
  errors and noise in the training data, preventing
  generalization.
• There is usually a trade-off between hypothesis
  complexity and degree of fit to the training
  data.
• Methods for preventing over-fitting
• Predetermined strong language bias.
• Pruning or early stopping criteria to prevent
  learning overly-complex hypotheses.

16
Learning Approaches
17
More Learning Approaches
18
Text Categorization

• Assigning documents to a fixed set of categories.
• Applications
• Web pages
• Recommending
• Yahoo-like classification
• Newsgroup Messages
• Recommending
• spam filtering
• News articles
• Personalized newspaper
• Email messages
• Routing
• Prioritizing
• Folderizing
• spam filtering

19
Relevance Feedback Architecture
Document corpus
Rankings
IR System
20
Using Relevance Feedback (Rocchio)

• Relevance feedback methods can be adapted for
  text categorization.
• Use standard TF/IDF weighted vectors to represent
  text documents (normalized by maximum term
  frequency).
• For each category, compute a prototype vector by
  summing the vectors of the training documents in
  the category.
• Assign test documents to the category with the
  closest prototype vector based on cosine
  similarity.

21
Illustration of Rocchio Text Categorization
22
Rocchio Text Categorization Algorithm(Training)
Assume the set of categories is c1, c2,cn For
i from 1 to n let pi lt0, 0,,0gt (init.
prototype vectors) For each training example ltx,
c(x)gt ? D Let d be the frequency normalized
TF/IDF term vector for doc x Let i j (cj
c(x)) (sum all the document vectors in
ci to get pi) Let pi pi d
23
Rocchio Text Categorization Algorithm(Test)
Given test document x Let d be the TF/IDF
weighted term vector for x Let m 2 (init.
maximum cosSim) For i from 1 to n (compute
similarity to prototype vector) Let s
cosSim(d, pi) if s gt m let m s
let r ci (update most similar class
prototype) Return class r
24
Rocchio Properties

• Does not guarantee a consistent hypothesis.
• Forms a simple generalization of the examples in
  each class (a prototype).
• Prototype vector does not need to be averaged or
  otherwise normalized for length since cosine
  similarity is insensitive to vector length.
• Classification is based on similarity to class
  prototypes.

25
Rocchio Time Complexity

• Note The time to add two sparse vectors is
  proportional to minimum number of non-zero
  entries in the two vectors.
• Training Time O(D(Ld Vd)) O(D Ld)
  where Ld is the average length of a document in D
  and Vd is the average vocabulary size for a
  document in D.
• Test Time O(Lt CVt)
  where Lt is the average length of a
  test document and Vt is the average vocabulary
  size for a test document.
• Assumes lengths of pi vectors are computed and
  stored during training, allowing cosSim(d, pi) to
  be computed in time proportional to the number
  of non-zero entries in d (i.e. Vt)

26
Nearest-Neighbor Learning Algorithm

• Learning is just storing the representations of
  the training examples in D.
• Testing instance x
• Compute similarity between x and all examples in
  D.
• Assign x the category of the most similar example
  in D.
• Does not explicitly compute a generalization or
  category prototypes.
• Also called
• Case-based
• Instance-based
• Memory-based
• Lazy learning

27
K Nearest-Neighbor

• Using only the closest example to determine
  categorization is subject to errors due to
• A single atypical example.
• Noise (i.e. error) in the category label of a
  single training example.
• More robust alternative is to find the k
  most-similar examples and return the majority
  category of these k examples.
• Value of k is typically odd to avoid ties, 3 and
  5 are most common.

28
Similarity Metrics

• Nearest neighbor method depends on a similarity
  (or distance) metric.
• Simplest for continuous m-dimensional instance
  space is Euclidian distance.
• Simplest for m-dimensional binary instance space
  is Hamming distance (number of feature values
  that differ).
• For text, cosine similarity of TF-IDF weighted
  vectors is typically most effective.

29
3 Nearest Neighbor Illustration(Euclidian
Distance)
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K Nearest Neighbor for Text
Training For each each training example ltx,
c(x)gt ? D Compute the corresponding TF-IDF
vector, dx, for document x Test instance
y Compute TF-IDF vector d for document y For
each ltx, c(x)gt ? D Let sx cosSim(d,
dx) Sort examples, x, in D by decreasing value of
sx Let N be the first k examples in D. (get
most similar neighbors) Return the majority class
of examples in N
31
Illustration of 3 Nearest Neighbor for Text
32
Rocchio Anomoly

• Prototype models have problems with polymorphic
  (disjunctive) categories.

33
3 Nearest Neighbor Comparison

• Nearest Neighbor tends to handle polymorphic
  categories better.

34
Nearest Neighbor Time Complexity

• Training Time O(D Ld) to compose TF-IDF
  vectors.
• Testing Time O(Lt DVt) to compare to all
  training vectors.
• Assumes lengths of dx vectors are computed and
  stored during training, allowing cosSim(d, dx) to
  be computed in time proportional to the number
  of non-zero entries in d (i.e. Vt)
• Testing time can be high for large training sets.

35
Nearest Neighbor with Inverted Index

• Determining k nearest neighbors is the same as
  determining the k best retrievals using the test
  document as a query to a database of training
  documents.
• Use standard VSR inverted index methods to find
  the k nearest neighbors.
• Testing Time O(BVt)
  where B is the average number of
  training documents in which a test-document word
  appears.
• Therefore, overall classification is O(Lt
  BVt)
• Typically B ltlt D

36
Bayesian Methods

• Learning and classification methods based on
  probability theory.
• Bayes theorem plays a critical role in
  probabilistic learning and classification.
• Uses prior probability of each category given no
  information about an item.
• Categorization produces a posterior probability
  distribution over the possible categories given a
  description of an item.

37
Conditional Probability

• P(A B) is the probability of A given B
• Assumes that B is all and only information known.
• Defined by

B
A
38
Independence

• A and B are independent iff
• Therefore, if A and B are independent

These two constraints are logically equivalent
39
Bayes Theorem

• Simple proof from definition of conditional
  probability

(Def. cond. prob.)
(Def. cond. prob.)
QED
40
Bayesian Categorization

• Let set of categories be c1, c2,cn
• Let E be description of an instance.
• Determine category of E by determining for each
  ci
• P(E) can be determined since categories are
  complete and disjoint.

41
Bayesian Categorization (cont.)

• Need to know
• Priors P(ci)
• Conditionals P(E ci)
• P(ci) are easily estimated from data.
• If ni of the examples in D are in ci,then P(ci)
  ni / D
• Assume instance is a conjunction of binary
  features
• Too many possible instances (exponential in m) to
  estimate all P(E ci)

42
Naïve Bayesian Categorization

• If we assume features of an instance are
  independent given the category (ci)
  (conditionally independent).
• Therefore, we then only need to know P(ej
  ci) for each feature and category.

43
Naïve Bayes Example

• C allergy, cold, well
• e1 sneeze e2 cough e3 fever
• E sneeze, cough, ?fever

44
Naïve Bayes Example (cont.)

• P(well E) (0.9)(0.1)(0.1)(0.99)/P(E)0.0089/P(
  E)
• P(cold E) (0.05)(0.9)(0.8)(0.3)/P(E)0.01/P(E)
  
• P(allergy E) (0.05)(0.9)(0.7)(0.6)/P(E)0.019/
  P(E)
• Most probable category allergy
• P(E) 0.0089 0.01 0.019 0.0379
• P(well E) 0.23
• P(cold E) 0.26
• P(allergy E) 0.50

Esneeze, cough, ?fever
45
Estimating Probabilities

• Normally, probabilities are estimated based on
  observed frequencies in the training data.
• If D contains ni examples in category ci, and nij
  of these ni examples contains feature ej, then
• However, estimating such probabilities from small
  training sets is error-prone.
• If due only to chance, a rare feature, ek, is
  always false in the training data, ?ci P(ek
  ci) 0.
• If ek then occurs in a test example, E, the
  result is that ?ci P(E ci) 0 and ?ci P(ci
  E) 0

46
Smoothing

• To account for estimation from small samples,
  probability estimates are adjusted or smoothed.
• Laplace smoothing using an m-estimate assumes
  that each feature is given a prior probability,
  p, that is assumed to have been previously
  observed in a virtual sample of size m.
• For binary features, p is simply assumed to be
  0.5.

47
Naïve Bayes for Text

• Modeled as generating a bag of words for a
  document in a given category by repeatedly
  sampling with replacement from a vocabulary V
  w1, w2,wm based on the probabilities P(wj
  ci).
• Smooth probability estimates with Laplace
  m-estimates assuming a uniform distribution over
  all words (p 1/V) and m V
• Equivalent to a virtual sample of seeing each
  word in each category exactly once.

48
Text Naïve Bayes Algorithm(Train)
Let V be the vocabulary of all words in the
documents in D For each category ci ? C
Let Di be the subset of documents in D in
category ci P(ci) Di / D Let
Ti be the concatenation of all the documents in
Di Let ni be the total number of word
occurrences in Ti For each word wj ? V
Let nij be the number of occurrences
of wj in Ti Let P(wi ci)
(nij 1) / (ni V)
49
Text Naïve Bayes Algorithm(Test)
Given a test document X Let n be the number of
word occurrences in X Return the category
where aj is the word occurring the jth position
in X
50
Naïve Bayes Time Complexity

• Training Time O(DLd CV))
  where Ld is the average length of a document in
  D.
• Assumes V and all Di , ni, and nij pre-computed
  in O(DLd) time during one pass through all of
  the data.
• Generally just O(DLd) since usually CV lt
  DLd
• Test Time O(C Lt)
  where Lt is the average length of a test
  document.
• Very efficient overall, linearly proportional to
  the time needed to just read in all the data.
• Similar to Rocchio time complexity.

51
Underflow Prevention

• Multiplying lots of probabilities, which are
  between 0 and 1 by definition, can result in
  floating-point underflow.
• Since log(xy) log(x) log(y), it is better to
  perform all computations by summing logs of
  probabilities rather than multiplying
  probabilities.
• Class with highest final un-normalized log
  probability score is still the most probable.

52
Naïve Bayes Posterior Probabilities

• Classification results of naïve Bayes (the class
  with maximum posterior probability) are usually
  fairly accurate.
• However, due to the inadequacy of the conditional
  independence assumption, the actual
  posterior-probability numerical estimates are
  not.
• Output probabilities are generally very close to
  0 or 1.

53
Evaluating Categorization

• Evaluation must be done on test data that are
  independent of the training data (usually a
  disjoint set of instances).
• Classification accuracy c/n where n is the total
  number of test instances and c is the number of
  test instances correctly classified by the
  system.
• Results can vary based on sampling error due to
  different training and test sets.
• Average results over multiple training and test
  sets (splits of the overall data) for the best
  results.

54
N-Fold Cross-Validation

• Ideally, test and training sets are independent
  on each trial.
• But this would require too much labeled data.
• Partition data into N equal-sized disjoint
  segments.
• Run N trials, each time using a different segment
  of the data for testing, and training on the
  remaining N?1 segments.
• This way, at least test-sets are independent.
• Report average classification accuracy over the N
  trials.
• Typically, N 10.

55
Learning Curves

• In practice, labeled data is usually rare and
  expensive.
• Would like to know how performance varies with
  the number of training instances.
• Learning curves plot classification accuracy on
  independent test data (Y axis) versus number of
  training examples (X axis).

56
N-Fold Learning Curves

• Want learning curves averaged over multiple
  trials.
• Use N-fold cross validation to generate N full
  training and test sets.
• For each trial, train on increasing fractions of
  the training set, measuring accuracy on the test
  data for each point on the desired learning curve.

57
Sample Document Corpus

• 600 science pages from the web.
• 200 random samples each from the Yahoo indices
  for biology, physics, and chemistry.

58
Sample Learning Curve(Yahoo Science Data)
59
Clustering

• Partition unlabeled examples into disjoint
  subsets of clusters, such that
• Examples within a cluster are very similar
• Examples in different clusters are very different
• Discover new categories in an unsupervised manner
  (no sample category labels provided).

60
Clustering Example
.
61
Hierarchical Clustering

• Build a tree-based hierarchical taxonomy
  (dendrogram) from a set of unlabeled examples.
• Recursive application of a standard clustering
  algorithm can produce a hierarchical clustering.

62
Aglommerative vs. Divisive Clustering

• Aglommerative (bottom-up) methods start with each
  example in its own cluster and iteratively
  combine them to form larger and larger clusters.
• Divisive (partitional, top-down) separate all
  examples immediately into clusters.

63
Direct Clustering Method

• Direct clustering methods require a specification
  of the number of clusters, k, desired.
• A clustering evaluation function assigns a
  real-value quality measure to a clustering.
• The number of clusters can be determined
  automatically by explicitly generating
  clusterings for multiple values of k and choosing
  the best result according to a clustering
  evaluation function.

64
Hierarchical Agglomerative Clustering (HAC)

• Assumes a similarity function for determining the
  similarity of two instances.
• Starts with all instances in a separate cluster
  and then repeatedly joins the two clusters that
  are most similar until there is only one cluster.
• The history of merging forms a binary tree or
  hierarchy.

65
HAC Algorithm
Start with all instances in their own
cluster. Until there is only one cluster
Among the current clusters, determine the two
clusters, ci and cj, that are most
similar. Replace ci and cj with a single
cluster ci ? cj
66
Cluster Similarity

• Assume a similarity function that determines the
  similarity of two instances sim(x,y).
• Cosine similarity of document vectors.
• How to compute similarity of two clusters each
  possibly containing multiple instances?
• Single Link Similarity of two most similar
  members.
• Complete Link Similarity of two least similar
  members.
• Group Average Average similarity between members.

67
Single Link Agglomerative Clustering

• Use maximum similarity of pairs
• Can result in straggly (long and thin) clusters
  due to chaining effect.
• Appropriate in some domains, such as clustering
  islands.

68
Single Link Example
69
Complete Link Agglomerative Clustering

• Use minimum similarity of pairs
• Makes more tight, spherical clusters that are
  typically preferable.

70
Complete Link Example
71
Computational Complexity

• In the first iteration, all HAC methods need to
  compute similarity of all pairs of n individual
  instances which is O(n2).
• In each of the subsequent n?2 merging iterations,
  it must compute the distance between the most
  recently created cluster and all other existing
  clusters.
• In order to maintain an overall O(n2)
  performance, computing similarity to each other
  cluster must be done in constant time.

72
Computing Cluster Similarity

• After merging ci and cj, the similarity of the
  resulting cluster to any other cluster, ck, can
  be computed by
• Single Link
• Complete Link

73
Group Average Agglomerative Clustering

• Use average similarity across all pairs within
  the merged cluster to measure the similarity of
  two clusters.
• Compromise between single and complete link.
• Averaged across all ordered pairs in the merged
  cluster instead of unordered pairs between the
  two clusters.

74
Computing Group Average Similarity

• Assume cosine similarity and normalized vectors
  with unit length.
• Always maintain sum of vectors in each cluster.
• Compute similarity of clusters in constant time

75
Non-Hierarchical Clustering

• Typically must provide the number of desired
  clusters, k.
• Randomly choose k instances as seeds, one per
  cluster.
• Form initial clusters based on these seeds.
• Iterate, repeatedly reallocating instances to
  different clusters to improve the overall
  clustering.
• Stop when clustering converges or after a fixed
  number of iterations.

76
K-Means

• Assumes instances are real-valued vectors.
• Clusters based on centroids, center of gravity,
  or mean of points in a cluster, c
• Reassignment of instances to clusters is based on
  distance to the current cluster centroids.

77
Distance Metrics

• Euclidian distance (L2 norm)
• L1 norm
• Cosine Similarity (transform to a distance by
  subtracting from 1)

78
K-Means Algorithm
Let d be the distance measure between
instances. Select k random instances s1, s2,
sk as seeds. Until clustering converges or other
stopping criterion For each instance xi
Assign xi to the cluster cj such that
d(xi, sj) is minimal. (Update the seeds to
the centroid of each cluster) For each
cluster cj sj ?(cj)
79
K Means Example(K2)
Reassign clusters
Converged!
80
Time Complexity

• Assume computing distance between two instances
  is O(m) where m is the dimensionality of the
  vectors.
• Reassigning clusters O(kn) distance
  computations, or O(knm).
• Computing centroids Each instance vector gets
  added once to some centroid O(nm).
• Assume these two steps are each done once for I
  iterations O(Iknm).
• Linear in all relevant factors, assuming a fixed
  number of iterations, more efficient than O(n2)
  HAC.

81
Seed Choice

• Results can vary based on random seed selection.
• Some seeds can result in poor convergence rate,
  or convergence to sub-optimal clusterings.
• Select good seeds using a heuristic or the
  results of another method.

82
Buckshot Algorithm

• Combines HAC and K-Means clustering.
• First randomly take a sample of instances of size
  ?n
• Run group-average HAC on this sample, which takes
  only O(n) time.
• Use the results of HAC as initial seeds for
  K-means.
• Overall algorithm is O(n) and avoids problems of
  bad seed selection.

83
Text Clustering

• HAC and K-Means have been applied to text in a
  straightforward way.
• Typically use normalized, TF/IDF-weighted vectors
  and cosine similarity.
• Optimize computations for sparse vectors.
• Applications
• During retrieval, add other documents in the same
  cluster as the initial retrieved documents to
  improve recall.
• Clustering of results of retrieval to present
  more organized results to the user (à la
  Northernlight folders).
• Automated production of hierarchical taxonomies
  of documents for browsing purposes (à la Yahoo
  DMOZ).

84
Soft Clustering

• Clustering typically assumes that each instance
  is given a hard assignment to exactly one
  cluster.
• Does not allow uncertainty in class membership or
  for an instance to belong to more than one
  cluster.
• Soft clustering gives probabilities that an
  instance belongs to each of a set of clusters.
• Each instance is assigned a probability
  distribution across a set of discovered
  categories (probabilities of all categories must
  sum to 1).

85
Expectation Maximization (EM)

• Probabilistic method for soft clustering.
• Direct method that assumes k clustersc1, c2,
  ck
• Soft version of k-means.
• Assumes a probabilistic model of categories that
  allows computing P(ci E) for each category, ci,
  for a given example, E.
• For text, typically assume a naïve-Bayes category
  model.
• Parameters ? P(ci), P(wj ci) i?1,k, j
  ?1,,V

86
EM Algorithm

• Iterative method for learning probabilistic
  categorization model from unsupervised data.
• Initially assume random assignment of examples to
  categories.
• Learn an initial probabilistic model by
  estimating model parameters ? from this randomly
  labeled data.
• Iterate following two steps until convergence
• Expectation (E-step) Compute P(ci E) for each
  example given the current model, and
  probabilistically re-label the examples based on
  these posterior probability estimates.
• Maximization (M-step) Re-estimate the model
  parameters, ?, from the probabilistically
  re-labeled data.

87
Learning from Probabilistically Labeled Data

• Instead of training data labeled with hard
  category labels, training data is labeled with
  soft probabilistic category labels.
• When estimating model parameters ? from training
  data, weight counts by the corresponding
  probability of the given category label.
• For example, if P(c1 E) 0.8 and P(c2 E)
  0.2, each word wj in E contributes only
  0.8 towards the counts n1 and n1j, and 0.2
  towards the counts n2 and n2j .

88
Naïve Bayes EM
Randomly assign examples probabilistic category
labels. Use standard naïve-Bayes training to
learn a probabilistic model with
parameters ? from the labeled data. Until
convergence or until maximum number of iterations
reached E-Step Use the naïve Bayes
model ? to compute P(ci E) for
each category and example, and re-label each
example using these probability
values as soft category labels. M-Step
Use standard naïve-Bayes training to re-estimate
the parameters ? using these new
probabilistic category labels.
89
Semi-Supervised Learning

• For supervised categorization, generating labeled
  training data is expensive.
• Idea Use unlabeled data to aid supervised
  categorization.
• Use EM in a semi-supervised mode by training EM
  on both labeled and unlabeled data.
• Train initial probabilistic model on user-labeled
  subset of data instead of randomly labeled
  unsupervised data.
• Labels of user-labeled examples are frozen and
  never relabeled during EM iterations.
• Labels of unsupervised data are constantly
  probabilistically relabeled by EM.

90
Semi-Supervised Example

• Assume quantum is present in several labeled
  physics documents, but Heisenberg occurs in
  none of the labeled data.
• From labeled data, learn that quantum is
  indicative of a physics document.
• When labeling unsupervised data, label several
  documents with quantum and Heisenberg
  correctly with the physics category.
• When retraining, learn that Heisenberg is also
  indicative of a physics document.
• Final learned model is able to correctly assign
  documents containing only Heisenberg to physics.

91
Semi-Supervision Results

• Experiments on assigning messages from 20 Usenet
  newsgroups their proper newsgroup label.
• With very few labeled examples (2 examples per
  class), semi-supervised EM improved accuracy from
  27 (supervised data only) to 43 (supervised
  unsupervised data).
• With more labeled examples, semi-supervision can
  actually decrease accuracy, but refinements to
  standard EM can prevent this.
• For semi-supervised EM to work, the natural
  clustering of data must be consistent with the
  desired categories.

92
Active Learning

• Select only the most informative examples for
  labeling.
• Initial methods
• Uncertainty sampling
• Committee-based sampling
• Error-reduction sampling

93
Weak Supervision

• Sometimes uncertain labeling can be inferred.
• Learning apprentices
• Inferred feedback
• Click patterns, reading time, non-verbal cues
• Delayed feedback
• Reinforcement learning
• Programming by Demonstration

94
Prior Knowledge

• Use of prior declarative knowledge in learning.
• Initial methods
• Explanation-based Learning
• Theory Refinement
• Bayesian Priors
• Reinforcement Learning with Advice

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Learning to Learn

• Many applications require learning for multiple,
  related problems.
• What can be learned from one problem that can aid
  the learning for other problems?
• Initial approaches
• Multi-task learning
• Life-long learning
• Learning similarity metrics
• Supra-classifiers