Various embodiments relate to a context recognition. Classification of a context is performed by using features received from at least one sensor of a client device, and model parameters being defined by a training data to output a result and a likelihood of the context. The result is shown to the user, who provides feedback regarding the result. The features, result, likelihood, and the feedback are stored, whereby the model parameters are adapted using the features, result, likelihood and the feedback to obtain adapted model parameters. The result, likelihood and the feedback can also be used for performing confidence estimation to obtain a confidence value. The confidence value can then be used for performing an action, e.g. adding a new sensor, adding a new feature, changing a device profile, launching an application.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-59. (canceled) 60. A method, comprising: performing classification of a context using features received from at least one sensor and model parameters being defined by a training data to output a result and a likelihood of the context; showing the result; obtaining feedback from the user regarding the result; storing the features, result, likelihood, and the feedback; and performing adaptation of the model parameters using the features, result, likelihood and the feedback to obtain adapted model parameters. 61. The method according to claim 60, further comprising evaluating a function ƒ, where the evaluation of the function ƒ comprises evaluating the likelihood values corresponding to yes and no answers against a threshold. 62. The method according to claim 61, where the function ƒ is in the form of f =  A  N  ( yes ) +  B  N  ( no ) , and where A={Lj(yes)|Lj(yes)>χ95} and B={Lj(no)|Lj(no)<χ95} and where |A| denotes the number of items in the set A; j is an index of the current class; Lj(no) is the set of likelihood values corresponding to observations with “no”-tag; N(no) is the total number of “no” answers; Lj(yes) is the set of likelihood values corresponding to observations with “yes”-tag; N(yes) is the total number of “yes” answers; the likelihood values Lj are defined as: Lj=(z−μj)TsjΣj−1(z−μj) and adapted class parameters are obtained from arg   min s j ∈ R + , μ j ∈ R N   f 63. The method according to claim 60, further comprising: communicating the features, result, likelihood and the feedback to another device; and receiving adapted model parameters from the other device. 64. The method according to claim 61, further comprising: minimizing the function ƒ; and stopping the adaptation when the function ƒ reaches the minimum. 65. The method according to the claim 60, further comprising: performing confidence estimation to obtain a confidence value using the result, likelihood and the feedback; and stopping the adaptation if the confidence value substantially matches the user feedback. 66. An apparatus comprising a processor, memory including computer program code, the memory and the computer program code configured to, with the processor, cause the apparatus to perform at least the following: perform classification of a context using features received from at least one sensor and model parameters being defined by a training data to output a result and a likelihood of the context; show the result; obtaining feedback from the user regarding the result; store the features, result, likelihood, and the feedback; and perform adaptation of the model parameters using the features, result, likelihood and the feedback to obtain adapted model parameters. 67. The apparatus according to claim 66, further comprising computer program code configured to, with the processor, cause the apparatus to perform at least the following: evaluate the likelihood values corresponding to yes and no answers against a threshold for the evaluation of a function ƒ. 68. The apparatus according to claim 67, where the function ƒ is in the form of f =  A  N  ( yes ) +  B  N  ( no ) , and where A={Lj(yes)|Lj(yes)>χ95} and B={Lj(no)|Lj(no)<χ95} and where |A| denotes the number of items in the set A; j is an index of the current class; Lj(no) is the set of likelihood values corresponding to observations with “no”-tag; N(no) is the total number of “no” answers; Lj(yes) is the set of likelihood values corresponding to observations with “yes”-tag; N(yes) is the total number of “yes” answers; the likelihood values Lj are defined as: Lj=(z−μj)TsjΣj−1(z−μj) and adapted class parameters are obtained from arg   min s j ∈ R + , μ j ∈ R N   f 69. The apparatus according to the claim 66, further comprising computer program code configured to, with the processor, cause the apparatus to perform at least the following: communicate the features, result, likelihood and the feedback to another device; and receive adapted model parameters from the other device. 70. The apparatus according to the claim 67, further comprising computer program code configured to, with the processor, cause the apparatus to perform at least the following: minimize the function ƒ; and stop the adaptation when the function ƒ reaches the minimum. 71. The apparatus according to the claim 66, further comprising computer program code configured to, with the processor, cause the apparatus to perform at least the following: perform confidence estimation to obtain a confidence value using the result, likelihood and the feedback; and stop the adaptation if the confidence value substantially matches the user feedback. 72. An apparatus comprising a processor, memory including computer program code, the memory and the computer program code configured to, with the processor, cause the apparatus to perform at least the following: perform a classification of a context using features received from at least one sensor and model parameters being defined by a training data to output a result and a likelihood; show the result; obtain feedback from the user regarding the result; storing the result, likelihood and the feedback; perform confidence estimation to obtain a confidence value using the result, likelihood and the feedback; and perform an action based on the confidence value. 73. A computer program embodied on a non-transitory computer readable medium, the computer program comprising instructions causing, when executed on at least one processor, at least one apparatus to: perform classification of a context using features received from at least one sensor and model parameters being defined by a training data to output a result and a likelihood of the context; show the result; obtain feedback from the user regarding the result; store the features, result, likelihood, and the feedback; and perform adaptation of the model parameters using the features, result, likelihood and the feedback to obtain adapted model parameters. 74. The computer program according to claim 73, wherein the apparatus is further caused to: evaluate a function ƒ, where the evaluation of the function ƒ comprises evaluating the likelihood values corresponding to yes and no answers against a threshold. 75. The computer program according to claim 74, where the function ƒ is in the form of f =  A  N  ( yes ) +  B  N  ( no ) , and where A={Lj(yes)|Lj(yes)>χ95} and B={Lj(no)|Lj(no)<χ95} and where |A| denotes the number of items in the set A; j is an index of the current class; Lj(no) is the set of likelihood values corresponding to observations with “no”-tag; N(no) is the total number of “no” answers; Lj(yes) is the set of likelihood values corresponding to observations with “yes”-tag; N(yes) is the total number of “yes” answers; the likelihood values Lj are defined as: Lj=(z−μj)TsjΣj−1(z−μj) and adapted class parameters are obtained from arg   min s j ∈ R + , μ j ∈ R N   f 76. The computer program according to claim 73, wherein the apparatus is further caused to: communicate the features, result, likelihood and the feedback to another device; and receive adapted model parameters from the other device. 77. The computer program according to claim 74, wherein the apparatus is further caused to: minimize the function ƒ; and stop the adaptation when the function ƒ reaches the minimum. 78. The computer program according to the claim 73, wherein the apparatus is further caused to: perform confidence estimation to obtain a confidence value using the result, likelihood and the feedback; and stop the adaptation if the confidence value substantially matches the user feedback. 79. A computer program embodied on a non-transitory computer readable medium, the computer program comprising instructions causing, when executed on at least one processor, at least one apparatus to: perform a classification of a context using features received from at least one sensor and model parameters being defined by a training data to output a result and a likelihood; show the result; obtain feedback from the user regarding the result; storing the result, likelihood and the feedback; perform confidence estimation to obtain a confidence value using the result, likelihood and the feedback; and perform an action based on the confidence value.