You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: The Management of Context-Sensitive Features: A Review of Strategies 
Chapter Title:  Introduction 
1. Abstract of Cited Paper (BIB001):  Abstract A method for constructing isomorphic context-specific connectionist networks for phoneme recognition is introduced. It is shown that such networks can be merged into a single context-modulated network that makes use of second-order unit interconnections. This is accomplished by computing a minimal basis for the set of context-specific weight vectors using the singular value decomposition algorithm. Compact networks are thus obtained in which the phoneme discrimination surfaces are modulated by phonetic context. These methods are demonstrated on a small but non-trivial vowel recognition problem. It is shown that a context-modulated network can achieve a lower error rate than a context-independent network by a factor of 7. Similar results are obtained using optimized rather than constructed networks. 
2. Abstract of Cited Paper (BIB002):  A method for speaker normalization and adaption using connectionist networks is developed. A speaker-specific linear transformation of observations of the speech signal is computed using second-order network units. Classification is accomplished by a multilayer feedforward network that operates on the normalized speech data. The network is adapted for a new talker by modifying the transformation parameters while leaving the classifier fixed. This is accomplished by backpropagating classification error through the classifier to the second-order transformation units. This method was evaluated for the classification of ten vowels for 76 speakers using the first two formant values of the Peterson-Barney data. The results suggest that rapid speaker adaptation resulting in high classification accuracy can be accomplished by this method. > 
3. Abstract of Cited Paper (BIB003):  A new, patient-adaptive ECG patient monitoring algorithm is described. The algorithm combines a patient-independent neural network classifier with a three-parameter patient model. The patient model is used to modulate the patient-independent classifier via multiplicative connections. Adaptation is carried out by gradient descent in the patient model parameter space. The patient-adaptive classifier was compared with a well-established baseline algorithm on six major databases, consisting of over 3 million heartbeats. When trained on an initial 77 records and tested on an additional 382 records, the patient-adaptive algorithm was found to reduce the number of Vn errors on one channel by a factor of 5, and the number of Nv errors by a factor of 10. We conclude that patient adaptation provides a significant advance in classifying normal vs. ventricular beats for ECG patient monitoring. 
4. Abstract of Cited Paper (BIB004):  The disclosure of this specification illustrates a GAP FILLER, designed to be easily and quickly removed from the gap between poured concrete sections, after the concrete sets. The individual plastic-foam slats, held together and attached to each other, are packaged in a watertight polyethylene bag, and are positioned against an existng concrete wall, ready to receive the second concrete pour. The zip-out filler will form the expansion joint between the two concrete pours. Removal of the filler is easily accomplished because of the inter-connected slats. 
5. Abstract of Cited Paper (BIB005):  This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the training set. The paper begins with a precise definition of the problem, then general strategies are presented for enhancing the performance of classification algorithms on this type of problem. These strategies are tested on two domains. The first domain is the diagnosis of gas turbine engines. The problem is to diagnose a faulty engine in one context, such as warm weather, when the fault has previously been seen only in another context, such as cold weather. The second domain is speech recognition. The problem is to recognize words spoken by a new speaker, not represented in the training set. For both domains, exploiting context results in substantially more accurate classification.

Literature Review:
This paper is concerned with the management of context for supervised machine learning from examples. We assume the standard machine learning framework, where examples are represented as vectors in a multidimensional feature space (also known as the attribute-value representation). We assume that a teacher has partitioned a set of training examples into a finite set of classes. It is the task of the machine learning system to induce a model for predicting the class of an example from its features. In many learning tasks, we may distinguish three different types of features: primary, contextual, and irrelevant features BIB006  . Primary features are useful for classification when considered in isolation, without regard for the other features. Contextual features are not useful in isolation, but can be useful when combined with other features. Irrelevant features are not useful for classification, either when considered alone or when combined with other features. We believe that primary features are often context-sensitive. That is, they may be useful for classification when considered in isolation, but the learning algorithm may perform even better when we take the contextual features into account. This paper is a survey of strategies for taking contextual features into account. The paper is motivated by the belief that contextual features are pervasive. In support of this claim, Table 1 lists some of the examples of contextual features that have been examined in the machine learning literature. Many standard machine learning datasets  contain contextual features, although this is rarely (explicitly) exploited. For example, in medical diagnosis problems, the patient's gender, age, and weight are often available. These features are contextual, since they (typically) do not influence the diagnosis when they are considered in isolation. In Section 2, we list five heuristic strategies for managing context. We often neglect context, because of its very ubiquity; however, it is sometimes possible to recover hidden (implicit, missing) contextual information. Section 3 discusses two techniques (clustering and time sequence) for exposing hidden context. Section 4 reviews evidence that hybrid strategies can perform better than the sum of the component strategies (synergy). Section 5 briefly surveys the literature on context-sensitive learning and shows how the work of various researchers fits into the framework we present here. We conclude in Section 6. Figure 1 illustrates our intuition about a common type of context-sensitivity. Let us consider a simple example: Suppose we are attempting to distinguish healthy people (class A) from sick people (class B), using an oral thermometer. Context 1 consists of temperature measurements made on people in the morning, after a good sleep. Context 2 consists of temperature measurements made on people after heavy exercise. Sick people tend to have higher temperatures than healthy people, but exercise also causes higher temperature. When the two contexts are considered separately, diagnosis is relatively simple. If we mix the contexts together, correct diagnosis becomes more difficult. BIB001 list four strategies for using contextual information when classifying. In earlier work BIB006  , we named these strategies contextual nor- BIB006  speech recognition sound spectrum information speaker's identity and gender BIB006  ,  hepatitis prognosis medical data patient's age  speech recognition sound spectrum information neighbouring phonemes BIB002 speech recognition sound spectrum information speaker's identity BIB003 heart disease diagnosis electrocardiogram data patient's identity BIB004 tonal music harmonization meter, tactus, local key to be discovered by the learner BIB005 malization, contextual expansion, contextual classifier selection, and contextual classification adjustment. In our previous work BIB006  , we discussed a strategy that was not included in the list of four strategies given by BIB001 . We called this strategy contextual weighting.